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+ "model_module_version": "1.5.0",
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+ "state": {
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+ "_model_module": "@jupyter-widgets/controls",
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+ "_model_module_version": "1.5.0",
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+ "_model_name": "DescriptionStyleModel",
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+ "_view_count": null,
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+ "_view_module": "@jupyter-widgets/base",
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+ }
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+ },
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+ "cells": [
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+ {
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+ "cell_type": "markdown",
1051
+ "source": [
1052
+ "Задача - определить диалект португальского языка по фразе.\n",
1053
+ "\n",
1054
+ "Данные взяты из субтитров кофнеренции TED"
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+ ],
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+ "metadata": {
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+ "id": "qF-Rki_SBT7-"
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+ }
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "source": [
1063
+ "### Подготовка данных"
1064
+ ],
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+ "metadata": {
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+ "id": "gjsF2U3a9_HR"
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+ }
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
1072
+ "!wget https://object.pouta.csc.fi/OPUS-TED2020/v1/mono/pt.txt.gz\n",
1073
+ "!wget https://object.pouta.csc.fi/OPUS-TED2020/v1/mono/pt_br.txt.gz\n",
1074
+ "!gunzip pt.txt.gz\n",
1075
+ "!gunzip pt_br.txt.gz\n"
1076
+ ],
1077
+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/"
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+ },
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+ "id": "XwRRwPFp9-JV",
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+ "outputId": "acfbaa31-8efd-45ed-cc42-8b124fac681e"
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+ },
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+ "execution_count": 2,
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+ "outputs": [
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+ {
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+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
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+ "--2023-04-15 10:19:27-- https://object.pouta.csc.fi/OPUS-TED2020/v1/mono/pt.txt.gz\n",
1091
+ "Resolving object.pouta.csc.fi (object.pouta.csc.fi)... 86.50.254.19\n",
1092
+ "Connecting to object.pouta.csc.fi (object.pouta.csc.fi)|86.50.254.19|:443... connected.\n",
1093
+ "HTTP request sent, awaiting response... 200 OK\n",
1094
+ "Length: 11610902 (11M) [application/gzip]\n",
1095
+ "Saving to: ‘pt.txt.gz’\n",
1096
+ "\n",
1097
+ "pt.txt.gz 100%[===================>] 11.07M 4.32MB/s in 2.6s \n",
1098
+ "\n",
1099
+ "2023-04-15 10:19:31 (4.32 MB/s) - ‘pt.txt.gz’ saved [11610902/11610902]\n",
1100
+ "\n",
1101
+ "--2023-04-15 10:19:31-- https://object.pouta.csc.fi/OPUS-TED2020/v1/mono/pt_br.txt.gz\n",
1102
+ "Resolving object.pouta.csc.fi (object.pouta.csc.fi)... 86.50.254.19\n",
1103
+ "Connecting to object.pouta.csc.fi (object.pouta.csc.fi)|86.50.254.19|:443... connected.\n",
1104
+ "HTTP request sent, awaiting response... 200 OK\n",
1105
+ "Length: 14802314 (14M) [application/gzip]\n",
1106
+ "Saving to: ‘pt_br.txt.gz’\n",
1107
+ "\n",
1108
+ "pt_br.txt.gz 100%[===================>] 14.12M 4.94MB/s in 2.9s \n",
1109
+ "\n",
1110
+ "2023-04-15 10:19:35 (4.94 MB/s) - ‘pt_br.txt.gz’ saved [14802314/14802314]\n",
1111
+ "\n"
1112
+ ]
1113
+ }
1114
+ ]
1115
+ },
1116
+ {
1117
+ "cell_type": "code",
1118
+ "source": [
1119
+ "import pandas as pd\n",
1120
+ "\n",
1121
+ "pt = pd.read_csv('pt.txt', sep='\\t', names=['text'])\n",
1122
+ "pt['target'] = 'pt'\n",
1123
+ "pt_br = pd.read_csv('pt_br.txt', sep='\\t', names=['text'])\n",
1124
+ "pt_br['target'] = 'pt_br'\n",
1125
+ "data = pd.concat([pt[:10000], pt_br[:10000]], ignore_index=True)"
1126
+ ],
1127
+ "metadata": {
1128
+ "id": "DIZD8gBB-6mo"
1129
+ },
1130
+ "execution_count": 35,
1131
+ "outputs": []
1132
+ },
1133
+ {
1134
+ "cell_type": "code",
1135
+ "source": [
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+ "data"
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+ ],
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+ "metadata": {
1139
+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 423
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+ "id": "FQdiHJihD46w",
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+ "outputId": "5de3ab8d-032a-4ed9-dab5-8961ad3b19e3"
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+ },
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+ "execution_count": 4,
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+ "outputs": [
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+ {
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+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": [
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+ " text target\n",
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+ "0 Em meados do século XVI, os italianos eram cat... pt\n",
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+ "1 Porém, este dom tinha um alto preço. pt\n",
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+ "2 Para impedir a alteração da voz, esses cantore... pt\n",
1156
+ "3 Conhecidos por \"castrati\", estas vozes agudas ... pt\n",
1157
+ "4 Embora o crescimento vocal retardado possa pro... pt\n",
1158
+ "... ... ...\n",
1159
+ "49995 Como você acha que seria o mundo se você fosse... pt_br\n",
1160
+ "49996 Bem, a resposta é nada. pt_br\n",
1161
+ "49997 A razão por que você não consegue enxergar no ... pt_br\n",
1162
+ "49998 Para ver uma maçã, a luz tem que atingir a maç... pt_br\n",
1163
+ "49999 Então, as retinas em seus olhos recebem o refl... pt_br\n",
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+ "[50000 rows x 2 columns]"
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+ " <div class=\"colab-df-container\">\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
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+ "\n",
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+ " .dataframe tbody tr th {\n",
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+ " }\n",
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+ "\n",
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+ " }\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <th>text</th>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>1</th>\n",
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+ " <td>Porém, este dom tinha um alto preço.</td>\n",
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+ " <tr>\n",
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+ " </tr>\n",
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+ " <th>3</th>\n",
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+ " <td>Conhecidos por \"castrati\", estas vozes agudas ...</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
1215
+ " <th>4</th>\n",
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+ " <td>Embora o crescimento vocal retardado possa pro...</td>\n",
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1225
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1226
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1227
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1228
+ " </tr>\n",
1229
+ " <tr>\n",
1230
+ " <th>49996</th>\n",
1231
+ " <td>Bem, a resposta é nada.</td>\n",
1232
+ " <td>pt_br</td>\n",
1233
+ " </tr>\n",
1234
+ " <tr>\n",
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+ " <th>49997</th>\n",
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+ " <td>A razão por que você não consegue enxergar no ...</td>\n",
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+ " </tr>\n",
1239
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1240
+ " <th>49998</th>\n",
1241
+ " <td>Para ver uma maçã, a luz tem que atingir a maç...</td>\n",
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+ " <th>49999</th>\n",
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+ " <td>Então, as retinas em seus olhos recebem o refl...</td>\n",
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+ " <td>pt_br</td>\n",
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+ "</table>\n",
1251
+ "<p>50000 rows × 2 columns</p>\n",
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+ "</div>\n",
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+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5ef419f-52cd-4661-92e3-493f6c907f3e')\"\n",
1254
+ " title=\"Convert this dataframe to an interactive table.\"\n",
1255
+ " style=\"display:none;\">\n",
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+ " \n",
1257
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1261
+ " </svg>\n",
1262
+ " </button>\n",
1263
+ " \n",
1264
+ " <style>\n",
1265
+ " .colab-df-container {\n",
1266
+ " display:flex;\n",
1267
+ " flex-wrap:wrap;\n",
1268
+ " gap: 12px;\n",
1269
+ " }\n",
1270
+ "\n",
1271
+ " .colab-df-convert {\n",
1272
+ " background-color: #E8F0FE;\n",
1273
+ " border: none;\n",
1274
+ " border-radius: 50%;\n",
1275
+ " cursor: pointer;\n",
1276
+ " display: none;\n",
1277
+ " fill: #1967D2;\n",
1278
+ " height: 32px;\n",
1279
+ " padding: 0 0 0 0;\n",
1280
+ " width: 32px;\n",
1281
+ " }\n",
1282
+ "\n",
1283
+ " .colab-df-convert:hover {\n",
1284
+ " background-color: #E2EBFA;\n",
1285
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1286
+ " fill: #174EA6;\n",
1287
+ " }\n",
1288
+ "\n",
1289
+ " [theme=dark] .colab-df-convert {\n",
1290
+ " background-color: #3B4455;\n",
1291
+ " fill: #D2E3FC;\n",
1292
+ " }\n",
1293
+ "\n",
1294
+ " [theme=dark] .colab-df-convert:hover {\n",
1295
+ " background-color: #434B5C;\n",
1296
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1297
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1298
+ " fill: #FFFFFF;\n",
1299
+ " }\n",
1300
+ " </style>\n",
1301
+ "\n",
1302
+ " <script>\n",
1303
+ " const buttonEl =\n",
1304
+ " document.querySelector('#df-f5ef419f-52cd-4661-92e3-493f6c907f3e button.colab-df-convert');\n",
1305
+ " buttonEl.style.display =\n",
1306
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1307
+ "\n",
1308
+ " async function convertToInteractive(key) {\n",
1309
+ " const element = document.querySelector('#df-f5ef419f-52cd-4661-92e3-493f6c907f3e');\n",
1310
+ " const dataTable =\n",
1311
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1312
+ " [key], {});\n",
1313
+ " if (!dataTable) return;\n",
1314
+ "\n",
1315
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
1316
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1317
+ " + ' to learn more about interactive tables.';\n",
1318
+ " element.innerHTML = '';\n",
1319
+ " dataTable['output_type'] = 'display_data';\n",
1320
+ " await google.colab.output.renderOutput(dataTable, element);\n",
1321
+ " const docLink = document.createElement('div');\n",
1322
+ " docLink.innerHTML = docLinkHtml;\n",
1323
+ " element.appendChild(docLink);\n",
1324
+ " }\n",
1325
+ " </script>\n",
1326
+ " </div>\n",
1327
+ " </div>\n",
1328
+ " "
1329
+ ]
1330
+ },
1331
+ "metadata": {},
1332
+ "execution_count": 4
1333
+ }
1334
+ ]
1335
+ },
1336
+ {
1337
+ "cell_type": "markdown",
1338
+ "source": [
1339
+ "### Файнтюн модели"
1340
+ ],
1341
+ "metadata": {
1342
+ "id": "u-03LX5EBtED"
1343
+ }
1344
+ },
1345
+ {
1346
+ "cell_type": "code",
1347
+ "execution_count": 5,
1348
+ "metadata": {
1349
+ "colab": {
1350
+ "base_uri": "https://localhost:8080/"
1351
+ },
1352
+ "id": "kgsVl04L9r0o",
1353
+ "outputId": "1933d7b0-0ea1-485c-a1b9-05066786f448"
1354
+ },
1355
+ "outputs": [
1356
+ {
1357
+ "output_type": "stream",
1358
+ "name": "stdout",
1359
+ "text": [
1360
+ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
1361
+ "Collecting transformers\n",
1362
+ " Downloading transformers-4.28.1-py3-none-any.whl (7.0 MB)\n",
1363
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m98.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1364
+ "\u001b[?25hCollecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
1365
+ " Downloading tokenizers-0.13.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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+ "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
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+ "Successfully installed huggingface-hub-0.13.4 tokenizers-0.13.3 transformers-4.28.1\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "!pip install transformers\n",
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+ "import transformers"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "!pip install datasets"
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/"
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+ "execution_count": 6,
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+ "outputs": [
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+ "Installing collected packages: xxhash, multidict, frozenlist, dill, async-timeout, yarl, responses, multiprocess, aiosignal, aiohttp, datasets\n",
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+ "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.11.0 dill-0.3.6 frozenlist-1.3.3 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 xxhash-3.2.0 yarl-1.8.2\n"
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+ ]
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+ }
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+ ]
1465
+ },
1466
+ {
1467
+ "cell_type": "code",
1468
+ "source": [
1469
+ "from sklearn.model_selection import train_test_split\n",
1470
+ "from transformers import AutoModelForSequenceClassification, AutoTokenizer, TrainingArguments, Trainer, IntervalStrategy\n",
1471
+ "from datasets import load_metric, Dataset, ClassLabel\n",
1472
+ "\n",
1473
+ "\n",
1474
+ "X_train, X_test, y_train, y_test = train_test_split(\n",
1475
+ " data,\n",
1476
+ " data['target'],\n",
1477
+ " test_size=0.1,\n",
1478
+ " random_state=42\n",
1479
+ ")\n",
1480
+ "\n",
1481
+ "tokenizer = AutoTokenizer.from_pretrained('adalbertojunior/distilbert-portuguese-cased', do_lower_case=False)\n",
1482
+ "\n",
1483
+ "class_label = ClassLabel(names=['pt','pt_br'])\n",
1484
+ "\n",
1485
+ "def tokenize(text):\n",
1486
+ " tokens = tokenizer(text['text'], padding=True, truncation=True, max_length=256, add_special_tokens = True)\n",
1487
+ " tokens['label'] = class_label.str2int(text['target'])\n",
1488
+ " return tokens\n",
1489
+ "\n",
1490
+ "train_ds = Dataset.from_pandas(X_train).map(tokenize, batched=True)\n",
1491
+ "test_ds = Dataset.from_pandas(X_test).map(tokenize, batched=True)\n",
1492
+ "\n",
1493
+ "train_ds.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])\n",
1494
+ "test_ds.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])"
1495
+ ],
1496
+ "metadata": {
1497
+ "colab": {
1498
+ "base_uri": "https://localhost:8080/",
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+ "height": 17,
1500
+ "referenced_widgets": [
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+ "79e7afed6f204ced8012f9853e8ce153",
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+ "92e3b0a9ee344022b0bf16a6617239b3",
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+ "09fa538b6a05498282c2438a6c854fd1",
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+ "3b905f68919049608a3eb3c48da12032",
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+ "eee35ecb15c84ee994f9ffa479feeaaf",
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+ "c17b6f3b3436490e923835add11f8d74",
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+ "4d5a02eb385d470f8b3c68a8db60561c",
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+ "7946964d5e8741ba86b2662397ccb891",
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+ "5624c10fc93943dda1e0c93c1fc2fbba",
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+ "101f117dfb0a441e807757524b857350",
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+ "77819fe20f514982b8547abff9361b2d",
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+ "8f76ba596bdc427aa868685b151f51d8",
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+ "a14ec2755a2a477aa654287af8a463a3",
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+ "cb82b162aae342888f353ea2fe507015",
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+ "ebb3365ce29e476ab28201e9b825e302",
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+ "31e7fabdde2f4129ab38d49b5c5b1d50",
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+ "b2fb5695afd04756975281c5a04fbcaf",
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+ "6e358ea9a32a486db0c81a5f54a112e9",
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+ "f439095945fb413e916059f01f67bc79",
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+ "fece699d83854b5e876164da8a3b6f3a",
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+ "814444ea270d4739aa8588937801800b"
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+ ]
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+ },
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+ "id": "Nf7Cew2HLKi6",
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+ "outputId": "f107d518-4016-4d4a-d3b8-161f4724bce4"
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+ },
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+ "execution_count": 46,
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+ "outputs": [
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+ {
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+ "output_type": "display_data",
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+ "data": {
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+ "text/plain": [
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+ ],
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+ "application/vnd.jupyter.widget-view+json": {
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+ "version_major": 2,
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+ "version_minor": 0,
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+ "model_id": "79e7afed6f204ced8012f9853e8ce153"
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+ }
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+ },
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+ "metadata": {}
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+ },
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+ {
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+ "text/plain": [
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+ "Map: 0%| | 0/2000 [00:00<?, ? examples/s]"
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+ ],
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+ "application/vnd.jupyter.widget-view+json": {
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+ "version_major": 2,
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+ "version_minor": 0,
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+ "model_id": "8f76ba596bdc427aa868685b151f51d8"
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+ }
1555
+ },
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+ "metadata": {}
1557
+ }
1558
+ ]
1559
+ },
1560
+ {
1561
+ "cell_type": "code",
1562
+ "source": [
1563
+ "from transformers import BertForSequenceClassification, AdamW, BertConfig\n",
1564
+ "\n",
1565
+ "model = BertForSequenceClassification.from_pretrained(\n",
1566
+ " 'adalbertojunior/distilbert-portuguese-cased',\n",
1567
+ " num_labels = 2,\n",
1568
+ " output_attentions = False, \n",
1569
+ " output_hidden_states = False,\n",
1570
+ ")"
1571
+ ],
1572
+ "metadata": {
1573
+ "colab": {
1574
+ "base_uri": "https://localhost:8080/",
1575
+ "height": 105,
1576
+ "referenced_widgets": [
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+ "00047affc56f494c82268e7a8bc2c53c",
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+ "dd7a5a0c04fa4b66a0b5078b0a3e59e0",
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+ "eeaf71899a6743a081f79552dfc1672b",
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+ "23f2d28b36c84ea78422ef020d0d5e4b",
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+ "02a6351920ba4a0689b19bb9531dc8df",
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+ "2859d235432e4d12a78f55412ba8c569",
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+ "7a8e9cb5072b47fa9a5cee174d29bc47",
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+ "b6f515df241e44f7ba04114423fc1f7e",
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+ "7fad565b40de40c2b3d8d72d8c50111e",
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+ "0c271f3f91314bd094d8db4834c90e41",
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+ "243a2bc40dbd48d2910616fa0e1a01a6"
1588
+ ]
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+ },
1590
+ "id": "xe7ooV6LXHrm",
1591
+ "outputId": "f4e0e785-ce91-4be3-cb19-675f94dc3f46"
1592
+ },
1593
+ "execution_count": 12,
1594
+ "outputs": [
1595
+ {
1596
+ "output_type": "display_data",
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+ "data": {
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+ "text/plain": [
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+ "Downloading pytorch_model.bin: 0%| | 0.00/266M [00:00<?, ?B/s]"
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+ ],
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+ "application/vnd.jupyter.widget-view+json": {
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+ "version_major": 2,
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+ "version_minor": 0,
1604
+ "model_id": "00047affc56f494c82268e7a8bc2c53c"
1605
+ }
1606
+ },
1607
+ "metadata": {}
1608
+ },
1609
+ {
1610
+ "output_type": "stream",
1611
+ "name": "stderr",
1612
+ "text": [
1613
+ "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at adalbertojunior/distilbert-portuguese-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
1614
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
1615
+ ]
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+ }
1617
+ ]
1618
+ },
1619
+ {
1620
+ "cell_type": "code",
1621
+ "source": [
1622
+ "import torch\n",
1623
+ "\n",
1624
+ "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
1625
+ "model.to(device)\n",
1626
+ "device"
1627
+ ],
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+ "metadata": {
1629
+ "colab": {
1630
+ "base_uri": "https://localhost:8080/",
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+ "height": 36
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+ },
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+ "id": "0PZkmqv9br7j",
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+ "outputId": "8710153b-357e-4116-93d7-ced770ef137b"
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+ },
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+ "execution_count": 14,
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+ "outputs": [
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+ {
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+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": [
1642
+ "'cuda'"
1643
+ ],
1644
+ "application/vnd.google.colaboratory.intrinsic+json": {
1645
+ "type": "string"
1646
+ }
1647
+ },
1648
+ "metadata": {},
1649
+ "execution_count": 14
1650
+ }
1651
+ ]
1652
+ },
1653
+ {
1654
+ "cell_type": "code",
1655
+ "source": [
1656
+ "params = list(model.named_parameters())\n",
1657
+ "\n",
1658
+ "print('The BERT model has {:} different named parameters.\\n'.format(len(params)))\n",
1659
+ "\n",
1660
+ "print('==== Embedding Layer ====\\n')\n",
1661
+ "\n",
1662
+ "for p in params[0:5]:\n",
1663
+ " print(\"{:<55} {:>12} {}\".format(p[0], str(tuple(p[1].size())), p[1].requires_grad))\n",
1664
+ "\n",
1665
+ "print('\\n==== First Transformer ====\\n')\n",
1666
+ "\n",
1667
+ "for p in params[5:21]:\n",
1668
+ " print(\"{:<55} {:>12} {}\".format(p[0], str(tuple(p[1].size())), p[1].requires_grad))\n",
1669
+ "\n",
1670
+ "print('\\n==== Output Layer ====\\n')\n",
1671
+ "\n",
1672
+ "for p in params[-4:]:\n",
1673
+ " print(\"{:<55} {:>12} {}\".format(p[0], str(tuple(p[1].size())), p[1].requires_grad))"
1674
+ ],
1675
+ "metadata": {
1676
+ "colab": {
1677
+ "base_uri": "https://localhost:8080/"
1678
+ },
1679
+ "id": "6KDOKbGCXHuQ",
1680
+ "outputId": "5d4cd088-3a0e-4be8-e7a4-427f3ba087c7"
1681
+ },
1682
+ "execution_count": 33,
1683
+ "outputs": [
1684
+ {
1685
+ "output_type": "stream",
1686
+ "name": "stdout",
1687
+ "text": [
1688
+ "The BERT model has 105 different named parameters.\n",
1689
+ "\n",
1690
+ "==== Embedding Layer ====\n",
1691
+ "\n",
1692
+ "bert.embeddings.word_embeddings.weight (29794, 768) False\n",
1693
+ "bert.embeddings.position_embeddings.weight (512, 768) False\n",
1694
+ "bert.embeddings.token_type_embeddings.weight (2, 768) False\n",
1695
+ "bert.embeddings.LayerNorm.weight (768,) False\n",
1696
+ "bert.embeddings.LayerNorm.bias (768,) False\n",
1697
+ "\n",
1698
+ "==== First Transformer ====\n",
1699
+ "\n",
1700
+ "bert.encoder.layer.0.attention.self.query.weight (768, 768) False\n",
1701
+ "bert.encoder.layer.0.attention.self.query.bias (768,) False\n",
1702
+ "bert.encoder.layer.0.attention.self.key.weight (768, 768) False\n",
1703
+ "bert.encoder.layer.0.attention.self.key.bias (768,) False\n",
1704
+ "bert.encoder.layer.0.attention.self.value.weight (768, 768) False\n",
1705
+ "bert.encoder.layer.0.attention.self.value.bias (768,) False\n",
1706
+ "bert.encoder.layer.0.attention.output.dense.weight (768, 768) False\n",
1707
+ "bert.encoder.layer.0.attention.output.dense.bias (768,) False\n",
1708
+ "bert.encoder.layer.0.attention.output.LayerNorm.weight (768,) False\n",
1709
+ "bert.encoder.layer.0.attention.output.LayerNorm.bias (768,) False\n",
1710
+ "bert.encoder.layer.0.intermediate.dense.weight (3072, 768) False\n",
1711
+ "bert.encoder.layer.0.intermediate.dense.bias (3072,) False\n",
1712
+ "bert.encoder.layer.0.output.dense.weight (768, 3072) False\n",
1713
+ "bert.encoder.layer.0.output.dense.bias (768,) False\n",
1714
+ "bert.encoder.layer.0.output.LayerNorm.weight (768,) False\n",
1715
+ "bert.encoder.layer.0.output.LayerNorm.bias (768,) False\n",
1716
+ "\n",
1717
+ "==== Output Layer ====\n",
1718
+ "\n",
1719
+ "bert.pooler.dense.weight (768, 768) False\n",
1720
+ "bert.pooler.dense.bias (768,) False\n",
1721
+ "classifier.weight (2, 768) True\n",
1722
+ "classifier.bias (2,) True\n"
1723
+ ]
1724
+ }
1725
+ ]
1726
+ },
1727
+ {
1728
+ "cell_type": "code",
1729
+ "source": [
1730
+ "for parameter in model.parameters():\n",
1731
+ " parameter.requires_grad = False\n",
1732
+ "for parameter in model.classifier.parameters():\n",
1733
+ " parameter.requires_grad = True"
1734
+ ],
1735
+ "metadata": {
1736
+ "id": "9DOy7eY5Y_px"
1737
+ },
1738
+ "execution_count": 16,
1739
+ "outputs": []
1740
+ },
1741
+ {
1742
+ "cell_type": "code",
1743
+ "source": [
1744
+ "import numpy as np\n",
1745
+ "\n",
1746
+ "training_args = TrainingArguments(\n",
1747
+ " output_dir=\"./trainer_out\",\n",
1748
+ " learning_rate=2e-4,\n",
1749
+ " per_device_train_batch_size=256,\n",
1750
+ " per_device_eval_batch_size=256,\n",
1751
+ " num_train_epochs=5,\n",
1752
+ " weight_decay=0.01,\n",
1753
+ " evaluation_strategy=IntervalStrategy.EPOCH,\n",
1754
+ ")\n",
1755
+ "\n",
1756
+ "metric = load_metric('accuracy')\n",
1757
+ "\n",
1758
+ "def compute_metrics(eval_pred):\n",
1759
+ " logits, labels = eval_pred\n",
1760
+ " predictions = np.argmax(logits, axis=-1)\n",
1761
+ " return metric.compute(predictions=predictions, references=labels)\n",
1762
+ "\n",
1763
+ "# # transfer learning\n",
1764
+ "# for module in list(model.modules())[:-3]:\n",
1765
+ "# for param in module.parameters():\n",
1766
+ "# param.requires_grad = False\n",
1767
+ "\n",
1768
+ "trainer = Trainer(\n",
1769
+ " model=model,\n",
1770
+ " args=training_args,\n",
1771
+ " train_dataset=train_ds,\n",
1772
+ " eval_dataset=test_ds,\n",
1773
+ " tokenizer=tokenizer,\n",
1774
+ " compute_metrics=compute_metrics,\n",
1775
+ ")\n",
1776
+ "\n",
1777
+ "trainer.train()"
1778
+ ],
1779
+ "metadata": {
1780
+ "colab": {
1781
+ "base_uri": "https://localhost:8080/",
1782
+ "height": 362
1783
+ },
1784
+ "id": "WiAQCmLZXHzy",
1785
+ "outputId": "a9f508ae-3eb8-4b7d-d333-193f2d236796"
1786
+ },
1787
+ "execution_count": 47,
1788
+ "outputs": [
1789
+ {
1790
+ "output_type": "stream",
1791
+ "name": "stderr",
1792
+ "text": [
1793
+ "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
1794
+ " warnings.warn(\n",
1795
+ "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
1796
+ ]
1797
+ },
1798
+ {
1799
+ "output_type": "display_data",
1800
+ "data": {
1801
+ "text/plain": [
1802
+ "<IPython.core.display.HTML object>"
1803
+ ],
1804
+ "text/html": [
1805
+ "\n",
1806
+ " <div>\n",
1807
+ " \n",
1808
+ " <progress value='355' max='355' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
1809
+ " [355/355 12:02, Epoch 5/5]\n",
1810
+ " </div>\n",
1811
+ " <table border=\"1\" class=\"dataframe\">\n",
1812
+ " <thead>\n",
1813
+ " <tr style=\"text-align: left;\">\n",
1814
+ " <th>Epoch</th>\n",
1815
+ " <th>Training Loss</th>\n",
1816
+ " <th>Validation Loss</th>\n",
1817
+ " <th>Accuracy</th>\n",
1818
+ " </tr>\n",
1819
+ " </thead>\n",
1820
+ " <tbody>\n",
1821
+ " <tr>\n",
1822
+ " <td>1</td>\n",
1823
+ " <td>No log</td>\n",
1824
+ " <td>0.634915</td>\n",
1825
+ " <td>0.663500</td>\n",
1826
+ " </tr>\n",
1827
+ " <tr>\n",
1828
+ " <td>2</td>\n",
1829
+ " <td>No log</td>\n",
1830
+ " <td>0.628911</td>\n",
1831
+ " <td>0.670500</td>\n",
1832
+ " </tr>\n",
1833
+ " <tr>\n",
1834
+ " <td>3</td>\n",
1835
+ " <td>No log</td>\n",
1836
+ " <td>0.625601</td>\n",
1837
+ " <td>0.669000</td>\n",
1838
+ " </tr>\n",
1839
+ " <tr>\n",
1840
+ " <td>4</td>\n",
1841
+ " <td>No log</td>\n",
1842
+ " <td>0.623909</td>\n",
1843
+ " <td>0.664000</td>\n",
1844
+ " </tr>\n",
1845
+ " <tr>\n",
1846
+ " <td>5</td>\n",
1847
+ " <td>No log</td>\n",
1848
+ " <td>0.623280</td>\n",
1849
+ " <td>0.664500</td>\n",
1850
+ " </tr>\n",
1851
+ " </tbody>\n",
1852
+ "</table><p>"
1853
+ ]
1854
+ },
1855
+ "metadata": {}
1856
+ },
1857
+ {
1858
+ "output_type": "execute_result",
1859
+ "data": {
1860
+ "text/plain": [
1861
+ "TrainOutput(global_step=355, training_loss=0.6338723196110255, metrics={'train_runtime': 724.7707, 'train_samples_per_second': 124.177, 'train_steps_per_second': 0.49, 'total_flos': 5961032939520000.0, 'train_loss': 0.6338723196110255, 'epoch': 5.0})"
1862
+ ]
1863
+ },
1864
+ "metadata": {},
1865
+ "execution_count": 47
1866
+ }
1867
+ ]
1868
+ },
1869
+ {
1870
+ "cell_type": "code",
1871
+ "source": [
1872
+ "trainer.train()"
1873
+ ],
1874
+ "metadata": {
1875
+ "colab": {
1876
+ "base_uri": "https://localhost:8080/",
1877
+ "height": 287
1878
+ },
1879
+ "id": "ounT1oDOniUI",
1880
+ "outputId": "50d5bd72-eab2-4be8-9f1d-04120b97cce9"
1881
+ },
1882
+ "execution_count": 53,
1883
+ "outputs": [
1884
+ {
1885
+ "output_type": "display_data",
1886
+ "data": {
1887
+ "text/plain": [
1888
+ "<IPython.core.display.HTML object>"
1889
+ ],
1890
+ "text/html": [
1891
+ "\n",
1892
+ " <div>\n",
1893
+ " \n",
1894
+ " <progress value='355' max='355' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
1895
+ " [355/355 12:01, Epoch 5/5]\n",
1896
+ " </div>\n",
1897
+ " <table border=\"1\" class=\"dataframe\">\n",
1898
+ " <thead>\n",
1899
+ " <tr style=\"text-align: left;\">\n",
1900
+ " <th>Epoch</th>\n",
1901
+ " <th>Training Loss</th>\n",
1902
+ " <th>Validation Loss</th>\n",
1903
+ " <th>Accuracy</th>\n",
1904
+ " </tr>\n",
1905
+ " </thead>\n",
1906
+ " <tbody>\n",
1907
+ " <tr>\n",
1908
+ " <td>1</td>\n",
1909
+ " <td>No log</td>\n",
1910
+ " <td>0.623280</td>\n",
1911
+ " <td>0.664500</td>\n",
1912
+ " </tr>\n",
1913
+ " <tr>\n",
1914
+ " <td>2</td>\n",
1915
+ " <td>No log</td>\n",
1916
+ " <td>0.623280</td>\n",
1917
+ " <td>0.664500</td>\n",
1918
+ " </tr>\n",
1919
+ " <tr>\n",
1920
+ " <td>3</td>\n",
1921
+ " <td>No log</td>\n",
1922
+ " <td>0.623280</td>\n",
1923
+ " <td>0.664500</td>\n",
1924
+ " </tr>\n",
1925
+ " <tr>\n",
1926
+ " <td>4</td>\n",
1927
+ " <td>No log</td>\n",
1928
+ " <td>0.623280</td>\n",
1929
+ " <td>0.664500</td>\n",
1930
+ " </tr>\n",
1931
+ " <tr>\n",
1932
+ " <td>5</td>\n",
1933
+ " <td>No log</td>\n",
1934
+ " <td>0.623280</td>\n",
1935
+ " <td>0.664500</td>\n",
1936
+ " </tr>\n",
1937
+ " </tbody>\n",
1938
+ "</table><p>"
1939
+ ]
1940
+ },
1941
+ "metadata": {}
1942
+ },
1943
+ {
1944
+ "output_type": "execute_result",
1945
+ "data": {
1946
+ "text/plain": [
1947
+ "TrainOutput(global_step=355, training_loss=0.6281896456866197, metrics={'train_runtime': 723.41, 'train_samples_per_second': 124.411, 'train_steps_per_second': 0.491, 'total_flos': 5961032939520000.0, 'train_loss': 0.6281896456866197, 'epoch': 5.0})"
1948
+ ]
1949
+ },
1950
+ "metadata": {},
1951
+ "execution_count": 53
1952
+ }
1953
+ ]
1954
+ },
1955
+ {
1956
+ "cell_type": "code",
1957
+ "source": [
1958
+ "trainer.save_model('./pt_br_model')"
1959
+ ],
1960
+ "metadata": {
1961
+ "id": "Bp7qkdjkXH2j"
1962
+ },
1963
+ "execution_count": 54,
1964
+ "outputs": []
1965
+ },
1966
+ {
1967
+ "cell_type": "code",
1968
+ "source": [
1969
+ "!tar jcpvf model.tar.bz2 pt_br_model"
1970
+ ],
1971
+ "metadata": {
1972
+ "colab": {
1973
+ "base_uri": "https://localhost:8080/"
1974
+ },
1975
+ "id": "Wmu5JQSue0Hb",
1976
+ "outputId": "54dae7e4-0c29-46ad-e304-04ab5703f1aa"
1977
+ },
1978
+ "execution_count": 55,
1979
+ "outputs": [
1980
+ {
1981
+ "output_type": "stream",
1982
+ "name": "stdout",
1983
+ "text": [
1984
+ "pt_br_model/\n",
1985
+ "pt_br_model/tokenizer_config.json\n",
1986
+ "pt_br_model/training_args.bin\n",
1987
+ "pt_br_model/vocab.txt\n",
1988
+ "pt_br_model/pytorch_model.bin\n",
1989
+ "pt_br_model/tokenizer.json\n",
1990
+ "pt_br_model/config.json\n",
1991
+ "pt_br_model/special_tokens_map.json\n"
1992
+ ]
1993
+ }
1994
+ ]
1995
+ },
1996
+ {
1997
+ "cell_type": "code",
1998
+ "source": [
1999
+ "!ls -al ./pt_br_model/*"
2000
+ ],
2001
+ "metadata": {
2002
+ "colab": {
2003
+ "base_uri": "https://localhost:8080/"
2004
+ },
2005
+ "id": "evbwLlLIi3QK",
2006
+ "outputId": "c1736d23-8d93-4597-ed76-3428d8d75e82"
2007
+ },
2008
+ "execution_count": 42,
2009
+ "outputs": [
2010
+ {
2011
+ "output_type": "stream",
2012
+ "name": "stdout",
2013
+ "text": [
2014
+ "-rw-r--r-- 1 root root 752 Apr 15 10:51 ./pt_br_model/config.json\n",
2015
+ "-rw-r--r-- 1 root root 265629621 Apr 15 10:51 ./pt_br_model/pytorch_model.bin\n",
2016
+ "-rw-r--r-- 1 root root 125 Apr 15 10:51 ./pt_br_model/special_tokens_map.json\n",
2017
+ "-rw-r--r-- 1 root root 395 Apr 15 10:51 ./pt_br_model/tokenizer_config.json\n",
2018
+ "-rw-r--r-- 1 root root 678043 Apr 15 10:51 ./pt_br_model/tokenizer.json\n",
2019
+ "-rw-r--r-- 1 root root 3579 Apr 15 10:51 ./pt_br_model/training_args.bin\n",
2020
+ "-rw-r--r-- 1 root root 209528 Apr 15 10:51 ./pt_br_model/vocab.txt\n"
2021
+ ]
2022
+ }
2023
+ ]
2024
+ },
2025
+ {
2026
+ "cell_type": "code",
2027
+ "source": [
2028
+ "!ls -al"
2029
+ ],
2030
+ "metadata": {
2031
+ "colab": {
2032
+ "base_uri": "https://localhost:8080/"
2033
+ },
2034
+ "id": "y68jWdM5jJJD",
2035
+ "outputId": "f9910474-5a0b-44da-b88d-ecaed8fae13a"
2036
+ },
2037
+ "execution_count": 44,
2038
+ "outputs": [
2039
+ {
2040
+ "output_type": "stream",
2041
+ "name": "stdout",
2042
+ "text": [
2043
+ "total 317924\n",
2044
+ "drwxr-xr-x 1 root root 4096 Apr 15 10:51 .\n",
2045
+ "drwxr-xr-x 1 root root 4096 Apr 15 10:18 ..\n",
2046
+ "drwxr-xr-x 4 root root 4096 Apr 13 13:29 .config\n",
2047
+ "-rw-r--r-- 1 root root 252222673 Apr 15 10:52 model.tar.bz2\n",
2048
+ "drwxr-xr-x 2 root root 4096 Apr 15 10:51 pt_br_model\n",
2049
+ "-rw-r--r-- 1 root root 41144160 Dec 3 2020 pt_br.txt\n",
2050
+ "-rw-r--r-- 1 root root 32155453 Dec 3 2020 pt.txt\n",
2051
+ "drwxr-xr-x 1 root root 4096 Apr 13 13:30 sample_data\n",
2052
+ "drwxr-xr-x 3 root root 4096 Apr 15 10:25 trainer_out\n"
2053
+ ]
2054
+ }
2055
+ ]
2056
+ },
2057
+ {
2058
+ "cell_type": "code",
2059
+ "source": [
2060
+ "!cp model.tar.bz2 /content/drive/MyDrive"
2061
+ ],
2062
+ "metadata": {
2063
+ "id": "_4BB3YnvRHZw"
2064
+ },
2065
+ "execution_count": 56,
2066
+ "outputs": []
2067
+ },
2068
+ {
2069
+ "cell_type": "code",
2070
+ "source": [
2071
+ "print(transformers.__version__)"
2072
+ ],
2073
+ "metadata": {
2074
+ "colab": {
2075
+ "base_uri": "https://localhost:8080/"
2076
+ },
2077
+ "id": "K-B-mwGryWOw",
2078
+ "outputId": "1742e546-04d3-47d7-e1bf-34da07ef35b8"
2079
+ },
2080
+ "execution_count": 58,
2081
+ "outputs": [
2082
+ {
2083
+ "output_type": "stream",
2084
+ "name": "stdout",
2085
+ "text": [
2086
+ "4.28.1\n"
2087
+ ]
2088
+ }
2089
+ ]
2090
+ }
2091
+ ]
2092
+ }