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train-rubert-tiny-sentiment-classifier.ipynb
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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/avidale/e678c5478086c1d1adc52a85cb2b93e6/train-rubert-tiny-sentiment-classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Rdyak4aXu-1p"
},
"source": [
"В этом блокноте мы обучаем маленький BERT распознавать в коротких русских текстах сентимент. Результирующая модель выложена в открытый доступ: https://huggingface.co/cointegrated/rubert-tiny-sentiment-balanced. \n",
"\n",
"Данные возьмём из проекта Сметанина: https://github.com/sismetanin/sentiment-analysis-in-russian\n",
"\n",
"Для удобства я вручную скачал все данные и сложил на гугл диск. Результирующий датасет (он собирается посередине этого блокнота) вы можете выкачать [тут](https://drive.google.com/file/d/1dir_lixYfReDXxRS5oGGljH8T_f7vVqm/view?usp=sharing).\n"
]
},
{
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},
"id": "lk251N2bkWpy",
"outputId": "35e18969-1d0d-4208-dbbd-cbba135e226b"
},
"source": [
"from google.colab import drive\n",
"drive.mount('/gd')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Drive already mounted at /gd; to attempt to forcibly remount, call drive.mount(\"/gd\", force_remount=True).\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rj4u_1ezcYxD",
"outputId": "0d92f90d-86e9-479f-d1d0-539a7a9a75da"
},
"source": [
"! pip install datasets transformers xmltodict --quiet"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[K |████████████████████████████████| 264 kB 5.0 MB/s \n",
"\u001b[K |████████████████████████████████| 2.6 MB 65.4 MB/s \n",
"\u001b[K |████████████████████████████████| 118 kB 68.6 MB/s \n",
"\u001b[K |████████████████████████████████| 243 kB 70.7 MB/s \n",
"\u001b[K |████████████████████████████████| 43 kB 2.5 MB/s \n",
"\u001b[K |████████████████████████████████| 895 kB 57.0 MB/s \n",
"\u001b[K |████████████████████████████████| 3.3 MB 61.1 MB/s \n",
"\u001b[K |████████████████████████████████| 636 kB 61.7 MB/s \n",
"\u001b[?25h"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "UpPaXkJBcqy-"
},
"source": [
"import pandas as pd\n",
"import torch"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "_qijKCotcizx"
},
"source": [
"# Load and unify data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jP5Uekwms_MC"
},
"source": [
"## SentiRuEval2016\n",
"\n",
"http://www.dialog-21.ru/evaluation/2016/sentiment/\n",
"\n",
"https://drive.google.com/drive/folders/0BxlA8wH3PTUfV1F1UTBwVTJPd3c?resourcekey=0-k9mcoCJ0D8bfaHa9h3fIWw"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nOpHZAnXkEMd",
"outputId": "c555f29d-639f-480f-9d17-dc6e061bea6a"
},
"source": [
"!ls /gd/MyDrive/datasets/nlp/sentiment/SentiRuEval_2016"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" banks_test_2016.xml\t tkk_test_2016.xml\n",
" banks_test_etalon.xml\t tkk_test_etalon.xml\n",
" bank_train_2016.xml\t tkk_train_2016.xml\n",
" eval\t\t\t'Результаты SentiRueval 2016.gsheet'\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "hQ9uN8wHkz8x"
},
"source": [
"import xmltodict"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "D7G-9nv2k1QE"
},
"source": [
"dirname = '/gd/MyDrive/datasets/nlp/sentiment/SentiRuEval_2016/'"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JwipkKagl2re",
"outputId": "cfd4238b-d56b-4de7-da1a-ceb7d82fec61"
},
"source": [
"with open(dirname + 'bank_train_2016.xml', 'r') as f:\n",
" data = xmltodict.parse(f.read())\n",
"objects = ['sberbank', 'vtb', 'gazprom', 'alfabank', 'bankmoskvy', 'raiffeisen', 'uralsib', 'rshb']\n",
"\n",
"senti_data_banks = []\n",
"for item in data['pma_xml_export']['database']['table']:\n",
" item_dict = {v['@name']: v['#text'] for v in item['column']}\n",
" for o in objects:\n",
" if item_dict[o] != 'NULL':\n",
" senti_data_banks.append({'text': item_dict['text'], 'label': int(item_dict[o]), 'object': o})\n",
"senti_data_banks = pd.DataFrame(senti_data_banks)\n",
"print(senti_data_banks.shape)\n",
"print(senti_data_banks.label.value_counts())"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(10725, 3)\n",
" 0 7158\n",
"-1 2807\n",
" 1 760\n",
"Name: label, dtype: int64\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yG66-1a6iVOI",
"outputId": "10bcb211-3a5e-40f2-da0e-b515c8471994"
},
"source": [
"def foo(x):\n",
" if x == 1:\n",
" return 'positive'\n",
" if x == 0:\n",
" return 'neutral'\n",
" if x == -1:\n",
" return 'negative'\n",
" return x\n",
"senti_data_banks.label = senti_data_banks.label.apply(foo)\n",
"senti_data_banks.label.value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"neutral 7158\n",
"negative 2807\n",
"positive 760\n",
"Name: label, dtype: int64"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Y7vo-9E1mzXr"
},
"source": [
"with open(dirname + 'tkk_train_2016.xml', 'r') as f:\n",
" data = xmltodict.parse(f.read())"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3xVCOiIrm2YQ",
"outputId": "1ace55bd-38b1-4bd9-f57c-88ebe7983aaa"
},
"source": [
"data['pma_xml_export']['database']['table'][0]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"OrderedDict([('@name', 'tkk_train_2016'),\n",
" ('column',\n",
" [OrderedDict([('@name', 'id'), ('#text', '1')]),\n",
" OrderedDict([('@name', 'twitid'),\n",
" ('#text', '492367588165680000')]),\n",
" OrderedDict([('@name', 'date'), ('#text', '1406224555')]),\n",
" OrderedDict([('@name', 'text'),\n",
" ('#text',\n",
" '@mkomov Максим, Вашем письмо мы получили. Наши сотрудники свяжутся с Вами завтра и направят запрос инженерам для проверки. #билайн')]),\n",
" OrderedDict([('@name', 'beeline'), ('#text', '0')]),\n",
" OrderedDict([('@name', 'mts'), ('#text', 'NULL')]),\n",
" OrderedDict([('@name', 'megafon'), ('#text', 'NULL')]),\n",
" OrderedDict([('@name', 'tele2'), ('#text', 'NULL')]),\n",
" OrderedDict([('@name', 'rostelecom'), ('#text', 'NULL')]),\n",
" OrderedDict([('@name', 'komstar'), ('#text', 'NULL')]),\n",
" OrderedDict([('@name', 'skylink'), ('#text', 'NULL')])])])"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "z2RTsZBIlSq9",
"outputId": "6c6fd41a-3764-4d0c-e956-0da9315bc345"
},
"source": [
"with open(dirname + 'tkk_train_2016.xml', 'r') as f:\n",
" data = xmltodict.parse(f.read())\n",
"objects = ['beeline', 'mts', 'megafon', 'tele2', 'rostelecom', 'komstar', 'skylink']\n",
"\n",
"senti_data_tele = []\n",
"for item in data['pma_xml_export']['database']['table']:\n",
" item_dict = {v['@name']: v['#text'] for v in item['column']}\n",
" for o in objects:\n",
" if item_dict[o] != 'NULL':\n",
" senti_data_tele.append({'text': item_dict['text'], 'label': int(item_dict[o]), 'object': o})\n",
"senti_data_tele = pd.DataFrame(senti_data_tele)\n",
"print(senti_data_tele.shape)\n",
"print(senti_data_tele.label.value_counts())"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(9209, 3)\n",
" 0 5213\n",
"-1 2611\n",
" 1 1385\n",
"Name: label, dtype: int64\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sYo6z8YYipDy",
"outputId": "4f1d710f-d136-4890-8586-046fbcc9c7c2"
},
"source": [
"def foo(x):\n",
" if x == 1:\n",
" return 'positive'\n",
" if x == 0:\n",
" return 'neutral'\n",
" if x == -1:\n",
" return 'negative'\n",
" return x\n",
"senti_data_tele.label = senti_data_tele.label.apply(foo)\n",
"senti_data_tele.label.value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"neutral 5213\n",
"negative 2611\n",
"positive 1385\n",
"Name: label, dtype: int64"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "W9t0Vjh25VOs"
},
"source": [
"senti_data_tele['source'] = 'SentiRuEval2016_tele'\n",
"senti_data_banks['source'] = 'SentiRuEval2016_banks'"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZYBcxwJvnZzs"
},
"source": [
"## SentiRuEval2015\n",
"\n",
"https://drive.google.com/drive/folders/1f2bIJ-JDxIRCI1gEdEdB1kMe7lGJK02m"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mVa1_ADzcZBA",
"outputId": "1f3cc7c7-5aae-4536-940f-33f14d334939"
},
"source": [
"!ls /gd/MyDrive/datasets/nlp/sentiment/SentiRuEval-2015"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Aspects_guidelines.doc\t\t SentiRuEval_car_markup_train.xml\n",
"eval\t\t\t\t SentiRuEval_rest_markup_test.xml\n",
"readme.txt\t\t\t SentiRuEval_rest_markup_train.xml\n",
"SentiRuEval_car_markup_test.xml SentiRuEval_results.gsheet\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ovJcvzIZqHUZ",
"outputId": "fac11c4d-ed72-429f-c02b-42d478d9a18c"
},
"source": [
"dirname = '/gd/MyDrive/datasets/nlp/sentiment/SentiRuEval-2015/'\n",
"with open(dirname + 'SentiRuEval_car_markup_train.xml', 'r') as f:\n",
" data = xmltodict.parse(f.read())\n",
"print(len(data['reviews']['review']))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"217\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jmVkRs6qdBkr",
"outputId": "1c5ae5e5-1634-40f5-b902-577f4967bcba"
},
"source": [
"dirname = '/gd/MyDrive/datasets/nlp/sentiment/SentiRuEval-2015/'\n",
"with open(dirname + 'SentiRuEval_rest_markup_train.xml', 'r') as f:\n",
" data = xmltodict.parse(f.read())\n",
"print(len(data['reviews']['review']))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"201\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zU5twqhDdauH"
},
"source": [
"Этот датасет направлен на анализ аспектов, а не текста в целом, самих текстов мало, и оценить их - сложно. Поэтому пока что забью на эту тему. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Hjgi2DS9t6vC"
},
"source": [
"## RuTweetCorp\n",
"\n",
"https://study.mokoron.com/#download\n",
"\n",
"Нейтральные тексты я сам насэмплил для единообразия. Для этого я обучил простой классификатор (логрег на символьных n-граммах) отличать позитивные и негативные твиты от каких попало твитов, и выбрал 200К случайных твитов, с высокой уверенностью классифицированных как \"что попало\". "
]
},
{
"cell_type": "code",
"metadata": {
"id": "5FdKuJr3t85-"
},
"source": [
"cols = ['id', 'date', 'username', 'text', 'label', 'rep', 'fav', 'stcount', 'fol', 'frien', 'listcount', 'hz']\n",
"tweet_pos = pd.read_csv('/gd/MyDrive/datasets/nlp/sentiment/mocoron/positive.csv', sep=';', header=None)\n",
"tweet_pos.columns = cols\n",
"tweet_neg = pd.read_csv('/gd/MyDrive/datasets/nlp/sentiment/mocoron/negative.csv', sep=';', header=None)\n",
"tweet_neg.columns = cols"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "305nYOM83Wn3"
},
"source": [
"tweet_neut = pd.read_csv('/gd/MyDrive/datasets/nlp/sentiment/mocoron/neutral.csv')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "g3CIJW48ffT2",
"outputId": "9f80e172-8cf3-40d6-df52-f90d406c1c73"
},
"source": [
"tweet_data = pd.DataFrame({\n",
" 'text': tweet_pos.text.tolist() + tweet_neg.text.tolist() + tweet_neut.text.tolist(),\n",
" 'label': ['positive'] * tweet_pos.shape[0] + ['negative'] * tweet_neg.shape[0] + ['neutral'] * tweet_neut.shape[0]\n",
"})\n",
"print(tweet_data.shape)\n",
"tweet_data.label.value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(519592, 2)\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"neutral 292758\n",
"positive 114911\n",
"negative 111923\n",
"Name: label, dtype: int64"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zM7oSLnO5eHW"
},
"source": [
"tweet_data['source'] = 'mokoron'"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "16_kctK-e6Hu"
},
"source": [
"## Linis"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iGdTveJtfGsh"
},
"source": [
"linis_data = pd.read_excel('/gd/MyDrive/datasets/nlp/sentiment/linis-crowd-doc_comment_summary.xlsx', header=None)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rViLK-Csfb1a",
"outputId": "c05c945f-2edf-408b-d827-9c2c17798771"
},
"source": [
"linis_data.columns = ['text', 'label']\n",
"linis_data.label.value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 13930\n",
"-1 9203\n",
"1 1795\n",
"-2 1534\n",
"2 365\n",
"GalinaPozd 6\n",
"Виталия Салина 5\n",
"Минченкова Елизавета 2\n",
"22158 2\n",
"23523 1\n",
"23486 1\n",
"Иван Мишалкин 1\n",
"Yudenkova Dasha 1\n",
"21887 1\n",
"Арина Макковеева 1\n",
"Анна Вейдер 1\n",
"Анна Аникина 1\n",
"Name: label, dtype: int64"
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bzHH1Uwof1bO",
"outputId": "b2e14bac-d0a4-4a23-f53f-84a3a8fbaf6a"
},
"source": [
"linis_data = linis_data[linis_data.label.apply(lambda x: x in {-2, -1, 0, 1, 2})].copy()\n",
"linis_data.label.value_counts().sort_index()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"-2 1534\n",
"-1 9203\n",
" 0 13930\n",
" 1 1795\n",
" 2 365\n",
"Name: label, dtype: int64"
]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nG1urC7u3lAk",
"outputId": "fd7e5a31-2df3-4513-975a-bd2f7f0975cb"
},
"source": [
"def foo(x):\n",
" if x >= 1:\n",
" return 'positive'\n",
" if x == 0:\n",
" return 'neutral'\n",
" if x <= -1:\n",
" return 'negative'\n",
" return x\n",
"linis_data.label = linis_data.label.apply(foo)\n",
"linis_data.label.value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"neutral 13930\n",
"negative 10737\n",
"positive 2160\n",
"Name: label, dtype: int64"
]
},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "AMhwo-Ab5h5w"
},
"source": [
"linis_data['source'] = 'linis'"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "u6sZ72-tgHXp"
},
"source": [
"## RuSentiment"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 159
},
"id": "hHVa1EJqgIh6",
"outputId": "aaefaaad-15fe-4f9c-d1b1-dc2e2aeb3176"
},
"source": [
"rusent_random = pd.read_csv('/gd/MyDrive/datasets/nlp/sentiment/Rusentiment/rusentiment_random_posts.csv')\n",
"print(rusent_random.shape)\n",
"rusent_random.sample(3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(21268, 2)\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>707</th>\n",
" <td>speech</td>\n",
" <td>С Днём рождения!!!\\nПусть у тебя ВСЁ получится)))</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>speech</td>\n",
" <td>С Днем Рождения! Всех благ и просветления!)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18330</th>\n",
" <td>neutral</td>\n",
" <td>КАКОЙ ВОЗРАСТ ВАШИХ ДЕТОК:</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label text\n",
"707 speech С Днём рождения!!!\\nПусть у тебя ВСЁ получится)))\n",
"565 speech С Днем Рождения! Всех благ и просветления!)\n",
"18330 neutral КАКОЙ ВОЗРАСТ ВАШИХ ДЕТОК:"
]
},
"metadata": {},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 159
},
"id": "K3OgtT28gh_D",
"outputId": "a0339041-8e33-480a-f7de-8dd8f6c24ce3"
},
"source": [
"rusent_active = pd.read_csv('/gd/MyDrive/datasets/nlp/sentiment/Rusentiment/rusentiment_preselected_posts.csv')\n",
"print(rusent_active.shape)\n",
"rusent_active.sample(3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(6950, 2)\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3686</th>\n",
" <td>positive</td>\n",
" <td>вот смешно!)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1143</th>\n",
" <td>neutral</td>\n",
" <td>Все социальные отношения строятся на лжи. Начн...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2077</th>\n",
" <td>neutral</td>\n",
" <td>Если победит невежество, то меня первого спаля...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label text\n",
"3686 positive вот смешно!)\n",
"1143 neutral Все социальные отношения строятся на лжи. Начн...\n",
"2077 neutral Если победит невежество, то меня первого спаля..."
]
},
"metadata": {},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Pzirbnv3gnyj",
"outputId": "994e379c-f8c8-47e8-af66-7595696b225d"
},
"source": [
"rusent_random.label.value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"neutral 8323\n",
"positive 4635\n",
"skip 3190\n",
"speech 2826\n",
"negative 2294\n",
"Name: label, dtype: int64"
]
},
"metadata": {},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jF2p-ueQhECC"
},
"source": [
"pd.options.display.max_colwidth = 300"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 546
},
"id": "REfFjPVZg9Zt",
"outputId": "75a3f889-dc68-4b1a-abc5-7e936ca47c1f"
},
"source": [
"rusent_random.groupby('label').sample(3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>226</th>\n",
" <td>negative</td>\n",
" <td>\\nРебята мы ведь можем по 4 часа в день трудиться. нас рабски используют.Платят медный грош или совсем забывают заплатить.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>negative</td>\n",
" <td>Раньше слово \"ЛЮБЛЮ\" было, как платье от Коко Шанель. Теперь, как дешевая майка с вьетнамского рынка</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19127</th>\n",
" <td>negative</td>\n",
" <td>Бедный Влад..</td>\n",
" </tr>\n",
" <tr>\n",
" <th>906</th>\n",
" <td>neutral</td>\n",
" <td>После работы посидел</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14041</th>\n",
" <td>neutral</td>\n",
" <td>ФУТБОЛЬЧИК МАЛЬЧИКИ ПОХОДЯТ НА КАЧКОВ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3830</th>\n",
" <td>neutral</td>\n",
" <td>Ребят, кто оформлял визу в Европу, а именно в Италию, Поделитесь контактами хорошего проверенного визового центра, пожалуйста! ASAP! 🙏🙏🙏</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2342</th>\n",
" <td>positive</td>\n",
" <td>наконецто пятница и мы уежаем в Харьков...STREET WORKOUT...!!!</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20865</th>\n",
" <td>positive</td>\n",
" <td>Верь в себя ;)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16093</th>\n",
" <td>positive</td>\n",
" <td>жду тебя, как воробей лета!</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6728</th>\n",
" <td>skip</td>\n",
" <td>з прошедчим</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3794</th>\n",
" <td>skip</td>\n",
" <td>чим маешся?</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16156</th>\n",
" <td>skip</td>\n",
" <td>Шел, говорят, к успеху шоколад :(</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9132</th>\n",
" <td>speech</td>\n",
" <td>С днём рождения, Настёна, поздравляю!\\nВ личный праздник от души тебе желаю:\\nЖить на полную катушку без оглядки,\\nПусть удача не играет с тобой в прятки.\\n\\nПусть доходною работа твоя будет,\\nПоцелуями любимый пусть будит.\\nКаждый день пусть будет ярок и не скушен,\\nВетер счастья будет пусть те...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6141</th>\n",
" <td>speech</td>\n",
" <td>С днём рождения!:)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11186</th>\n",
" <td>speech</td>\n",
" <td>С новым годом дорогая***) люблю скучаю**)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label text\n",
"226 negative \\nРебята мы ведь можем по 4 часа в день трудиться. нас рабски используют.Платят медный грош или совсем забывают заплатить.\n",
"140 negative Раньше слово \"ЛЮБЛЮ\" было, как платье от Коко Шанель. Теперь, как дешевая майка с вьетнамского рынка\n",
"19127 negative Бедный Влад..\n",
"906 neutral После работы посидел\n",
"14041 neutral ФУТБОЛЬЧИК МАЛЬЧИКИ ПОХОДЯТ НА КАЧКОВ\n",
"3830 neutral Ребят, кто оформлял визу в Европу, а именно в Италию, Поделитесь контактами хорошего проверенного визового центра, пожалуйста! ASAP! 🙏🙏🙏\n",
"2342 positive наконецто пятница и мы уежаем в Харьков...STREET WORKOUT...!!!\n",
"20865 positive Верь в себя ;)\n",
"16093 positive жду тебя, как воробей лета!\n",
"6728 skip з прошедчим\n",
"3794 skip чим маешся?\n",
"16156 skip Шел, говорят, к успеху шоколад :(\n",
"9132 speech С днём рождения, Настёна, поздравляю!\\nВ личный праздник от души тебе желаю:\\nЖить на полную катушку без оглядки,\\nПусть удача не играет с тобой в прятки.\\n\\nПусть доходною работа твоя будет,\\nПоцелуями любимый пусть будит.\\nКаждый день пусть будет ярок и не скушен,\\nВетер счастья будет пусть те...\n",
"6141 speech С днём рождения!:)\n",
"11186 speech С новым годом дорогая***) люблю скучаю**)"
]
},
"metadata": {},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ValAwlxOgpkX",
"outputId": "0ad19f21-a3fd-4eb8-81cc-eb31a80601f5"
},
"source": [
"rusent_active.label.value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"neutral 2977\n",
"positive 1475\n",
"negative 1360\n",
"skip 904\n",
"speech 234\n",
"Name: label, dtype: int64"
]
},
"metadata": {},
"execution_count": 31
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tCMVxU3y3xcn",
"outputId": "f0e05827-286b-4018-dbbc-a80499475b53"
},
"source": [
"rusent_data = pd.concat([rusent_active, rusent_random], ignore_index=True)\n",
"rusent_data = rusent_data[rusent_data.label.apply(lambda x: x in {'neutral', 'negative', 'positive'})].copy()\n",
"rusent_data.label.value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"neutral 11300\n",
"positive 6110\n",
"negative 3654\n",
"Name: label, dtype: int64"
]
},
"metadata": {},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "DSO4vGYm5mDC"
},
"source": [
"rusent_data['source'] = 'rusentiment'"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "30O3EovphOzr"
},
"source": [
"## Kaggle data"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RVRXIbPOhRJk",
"outputId": "a4972655-3fb4-41cb-95ad-1216278a1576"
},
"source": [
"kaggle_df = pd.read_json('/gd/MyDrive/datasets/nlp/sentiment/kaggle-sentiment-analysis-in-russian/train.json')\n",
"print(kaggle_df.shape)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(8263, 3)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5mUM0pJvhiWe",
"outputId": "ec50a04c-ed60-4b91-d9f0-70091442791f"
},
"source": [
"kaggle_df.sentiment.value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"neutral 4034\n",
"positive 2795\n",
"negative 1434\n",
"Name: sentiment, dtype: int64"
]
},
"metadata": {},
"execution_count": 35
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 243
},
"id": "h4bn7whjhb1Z",
"outputId": "f39e45e9-8b3f-46e5-f453-43782ca35095"
},
"source": [
"kaggle_df.sample(3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>text</th>\n",
" <th>id</th>\n",
" <th>sentiment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2934</th>\n",
" <td>Астана. 28 декабря. Kazakhstan Today - Движение поездов на участке Караганда - Астана, нарушенное из-за схода электровоза и 12 грузовых вагонов, восстановлено, сообщила пресс-служба АО \"НК \"Казакстан темір жолы\".\\n\"27 декабря текущего года в 23.00 движение поездов по станции Бабатай на участке К...</td>\n",
" <td>4988</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4255</th>\n",
" <td>\\n\\nВладельцы старых авто смогут сдать транспортное средство категории М1 (легковой автомобиль) на дальнейшую утилизацию и взамен получить денежную компенсацию, сообщает primeminister.kz. Стоит отметить, что транспортное средство будет оцениваться по двум категориям: полной комплектации – 150 ты...</td>\n",
" <td>6309</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4993</th>\n",
" <td>С декабря ОТЛК формирует контейнерные поезда по новому маршруту Гамбург - Чунцин в направлении Европа-Китай на еженедельной основе, говорится в сообщении. \\n \\n У ОАО \"Российские железные дороги\" 99,84% акций в ОТЛК, у АО \"Национальная компания \"Казахстан темир жолы\" (Казахстанские железные доро...</td>\n",
" <td>7047</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text ... sentiment\n",
"2934 Астана. 28 декабря. Kazakhstan Today - Движение поездов на участке Караганда - Астана, нарушенное из-за схода электровоза и 12 грузовых вагонов, восстановлено, сообщила пресс-служба АО \"НК \"Казакстан темір жолы\".\\n\"27 декабря текущего года в 23.00 движение поездов по станции Бабатай на участке К... ... neutral\n",
"4255 \\n\\nВладельцы старых авто смогут сдать транспортное средство категории М1 (легковой автомобиль) на дальнейшую утилизацию и взамен получить денежную компенсацию, сообщает primeminister.kz. Стоит отметить, что транспортное средство будет оцениваться по двум категориям: полной комплектации – 150 ты... ... positive\n",
"4993 С декабря ОТЛК формирует контейнерные поезда по новому маршруту Гамбург - Чунцин в направлении Европа-Китай на еженедельной основе, говорится в сообщении. \\n \\n У ОАО \"Российские железные дороги\" 99,84% акций в ОТЛК, у АО \"Национальная компания \"Казахстан темир жолы\" (Казахстанские железные доро... ... neutral\n",
"\n",
"[3 rows x 3 columns]"
]
},
"metadata": {},
"execution_count": 36
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "NCKmZAdf4FZs"
},
"source": [
"kaggle_data = kaggle_df[['text', 'sentiment']].copy()\n",
"kaggle_data.columns = ['text', 'label']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "TubhEmFA5qLX"
},
"source": [
"kaggle_data['source'] = 'kaggle_news'"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "qajRzhQ9htFZ"
},
"source": [
"## RuReviews"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 230
},
"id": "dgnHsCQ9hxkq",
"outputId": "29b4ae25-5e72-4f14-fe30-1124126d8578"
},
"source": [
"rureviews_data = pd.read_csv('/gd/MyDrive/datasets/nlp/sentiment/rureviews-women-clothing-accessories.3-class.balanced.csv', sep='\\t')\n",
"print(rureviews_data.shape)\n",
"print(rureviews_data.sentiment.value_counts())\n",
"rureviews_data.sample(3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(90000, 2)\n",
"neautral 30000\n",
"negative 30000\n",
"positive 30000\n",
"Name: sentiment, dtype: int64\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>review</th>\n",
" <th>sentiment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>76394</th>\n",
" <td>За свои деньги отлично. На рост 170 подошли. Отлично тянутся</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73217</th>\n",
" <td>Футболка, хорошего качества, дошла быстро! спасибо!</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74278</th>\n",
" <td>заказом довольна за такие деньги местами нитки торчат но все швы прошиты правда пакет пришел порваный но вещь не повредилась</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" review sentiment\n",
"76394 За свои деньги отлично. На рост 170 подошли. Отлично тянутся positive\n",
"73217 Футболка, хорошего качества, дошла быстро! спасибо! positive\n",
"74278 заказом довольна за такие деньги местами нитки торчат но все швы прошиты правда пакет пришел порваный но вещь не повредилась positive"
]
},
"metadata": {},
"execution_count": 39
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9F3oxEU-4N_U",
"outputId": "858c801b-5ce2-4a64-ead0-ba9d4209e3b1"
},
"source": [
"rureviews_data.columns = ['text', 'label']\n",
"def foo(x):\n",
" if x == 'neautral':\n",
" return 'neutral'\n",
" return x\n",
"rureviews_data.label = rureviews_data.label.apply(foo)\n",
"rureviews_data.label.value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"negative 30000\n",
"positive 30000\n",
"neutral 30000\n",
"Name: label, dtype: int64"
]
},
"metadata": {},
"execution_count": 40
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9InmnKb957rC"
},
"source": [
"rureviews_data['source'] = 'rureviews'"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "_pUKbCyxipTn"
},
"source": [
"# Join data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "GewF8UR76OyD"
},
"source": [
"all_dfs = [senti_data_tele, senti_data_banks, tweet_data, linis_data, rusent_data, kaggle_data, rureviews_data]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3r8o7UPD4egQ"
},
"source": [
"full_sentiment_data = pd.concat([df[['text', 'label', 'source']] for df in all_dfs], ignore_index=True).dropna()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qkVC7St-6Wgg",
"outputId": "4096e89a-4c02-4b09-ede6-c5c48ceacd5c"
},
"source": [
"print(full_sentiment_data.shape)\n",
"full_sentiment_data.source.value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(685605, 3)\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"mokoron 519592\n",
"rureviews 90000\n",
"linis 26752\n",
"rusentiment 21064\n",
"SentiRuEval2016_banks 10725\n",
"SentiRuEval2016_tele 9209\n",
"kaggle_news 8263\n",
"Name: source, dtype: int64"
]
},
"metadata": {},
"execution_count": 63
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_-uY6A8M6cvQ",
"outputId": "3b0b5b35-7954-44d1-ecd6-64e848fb8bf3"
},
"source": [
"full_sentiment_data.label.value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"neutral 364321\n",
"negative 163164\n",
"positive 158120\n",
"Name: label, dtype: int64"
]
},
"metadata": {},
"execution_count": 64
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TfbkFpT76ktu"
},
"source": [
"import random\n",
"\n",
"def do_split(x):\n",
" if random.random() < 0.8:\n",
" return 'train'\n",
" if random.random() < 0.5:\n",
" return 'dev'\n",
" return 'test'\n",
"\n",
"random.seed(1)\n",
"full_sentiment_data['split'] = full_sentiment_data.text.apply(do_split)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-5V7UBGJ7xhp",
"outputId": "049eddc6-2f1a-4c82-af1b-3e032365970c"
},
"source": [
"full_sentiment_data['split'].head(20)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 train\n",
"1 test\n",
"2 train\n",
"3 train\n",
"4 train\n",
"5 train\n",
"6 train\n",
"7 train\n",
"8 train\n",
"9 dev\n",
"10 train\n",
"11 train\n",
"12 train\n",
"13 train\n",
"14 train\n",
"15 test\n",
"16 train\n",
"17 train\n",
"18 train\n",
"19 dev\n",
"Name: split, dtype: object"
]
},
"metadata": {},
"execution_count": 66
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xw37Ull470KH"
},
"source": [
"```\n",
"0 train\n",
"1 test\n",
"2 train\n",
"3 train\n",
"4 train\n",
"5 train\n",
"6 train\n",
"7 train\n",
"8 train\n",
"9 dev\n",
"10 train\n",
"11 train\n",
"12 train\n",
"13 train\n",
"14 train\n",
"15 test\n",
"16 train\n",
"17 train\n",
"18 train\n",
"19 dev```"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Bs-STWmq66nA"
},
"source": [
"full_sentiment_data.to_csv('sentiment_data.tsv', sep='\\t', index=None)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "pU_rusy6haj7"
},
"source": [
"# Load the joint data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QkeSY0z10Csq"
},
"source": [
"full_sentiment_data = pd.read_csv('/gd/MyDrive/datasets/nlp/sentiment/joint_sentiment_data.tsv', sep='\\t')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "xIPdBms_iq1m"
},
"source": [
"from datasets import Dataset, DatasetDict"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "oQbJUETP1UwO"
},
"source": [
"dev_balanced = full_sentiment_data[full_sentiment_data.split=='dev'].groupby(['source', 'label']).sample(70, random_state=1)\n",
"torch_dev_balanced = Dataset.from_pandas(\n",
" dev_balanced[['text', 'label']].reset_index(drop=True)\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cCWeZ3PQlOYa",
"outputId": "4e941505-8fbb-4825-9450-5e0b8a1098b4"
},
"source": [
"train_data = full_sentiment_data[full_sentiment_data.split=='train'].dropna().reset_index(drop=True)\n",
"train_data.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(548422, 4)"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "FMLxOmJsiwtz",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d89898a8-1c94-4e05-fe5f-ba63b56d5b2d"
},
"source": [
"torch_data = DatasetDict({\n",
" 'train': Dataset.from_pandas(train_data[['text', 'label']].reset_index(drop=True)),\n",
" 'dev': Dataset.from_pandas(full_sentiment_data[full_sentiment_data.split=='dev'][['text', 'label']].reset_index(drop=True))\n",
"})\n",
"torch_data"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['text', 'label'],\n",
" num_rows: 548422\n",
" })\n",
" dev: Dataset({\n",
" features: ['text', 'label'],\n",
" num_rows: 68676\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_p3xlOQQguOh"
},
"source": [
"# Modelling"
]
},
{
"cell_type": "code",
"metadata": {
"id": "LYDD_J0zhQr2"
},
"source": [
"all_labels = ['negative', 'neutral', 'positive']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "cxjkjUkJ0mRj"
},
"source": [
"model_checkpoint = \"cointegrated/rubert-tiny\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "fpGVTIc4g4VT"
},
"source": [
"from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "uTA34QqZhh9B"
},
"source": [
"from transformers import DataCollatorWithPadding\n",
"data_collator = DataCollatorWithPadding(tokenizer)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "TACYuj_3leR6"
},
"source": [
"from torch.utils.data import DataLoader"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7Bk1dpMt0VSe"
},
"source": [
"import gc\n",
"\n",
"def cleanup():\n",
" gc.collect()\n",
" torch.cuda.empty_cache()\n",
" \n",
"cleanup()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hPR2akpOn8SC"
},
"source": [
"from tqdm.auto import tqdm, trange\n",
"import numpy as np\n",
"from sklearn.metrics import roc_auc_score, precision_recall_fscore_support\n",
"from IPython.display import display\n",
"\n",
"def evaluate_model(model, dev_dataloader, verbose=False, labels=None):\n",
" facts, preds = predict_with_model(model, dev_dataloader)\n",
" pfrs, aucs = get_classification_report(facts, preds, labels)\n",
" if verbose:\n",
" display(pfrs)\n",
" print('aucs:', aucs, np.mean(aucs))\n",
" return np.mean(aucs)\n",
"\n",
"def predict_with_model(model, dataloader):\n",
" preds = []\n",
" facts = []\n",
"\n",
" for batch in tqdm(dataloader):\n",
" facts.append(batch.labels.cpu().numpy())\n",
" batch = batch.to(model.device)\n",
" with torch.no_grad():\n",
" pr = model(input_ids=batch.input_ids, attention_mask=batch.attention_mask, token_type_ids=batch.token_type_ids)\n",
" preds.append(torch.softmax(pr.logits, -1).cpu().numpy())\n",
" facts = np.concatenate(facts)\n",
" preds = np.concatenate(preds)\n",
" return facts, preds\n",
"\n",
"def get_classification_report(facts, preds, labels=None):\n",
" pfrs = pd.DataFrame(dict(zip(['p', 'r', 'f', 's'], precision_recall_fscore_support(facts, preds.argmax(1)))))\n",
" aucs = [roc_auc_score(facts==i, preds[:, i]) for i in set(facts)]\n",
" pfrs['a'] = aucs\n",
" pfrs = pfrs.append(pfrs.mean(), ignore_index=True)\n",
" if labels is not None:\n",
" pfrs.index = list(labels) + ['mean']\n",
" return pfrs, aucs"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"37596a0a5207491a9820f377e26614d0",
"54d6843a06a2423f992610833b1636f2",
"5d3e486671d34ed3bbf10c0bbaaaa00f",
"914377b95dfa46e2a9b12d4873444d78",
"e7672fa173fa4a949008c194fbb80701",
"9be43bb395a54a8abe2f204d80817fca",
"812159f8bef0456897f90fd8a1f69d5b",
"af0ab8e0cb7e48c8b3800a4bb49c70ff",
"9fb5a0d592fe414c9fafcbff37dd94ce",
"3a1eab3faf19427fa5bb2a6591524056",
"0c3df857d5e04a96aa404f169e93500d",
"a5465269d8674da39588a776efe37280",
"d6e7906da28e4630a7bd7982ee191609",
"8461f811450d41b491d1a58f94338d22",
"af8cf41fa15e49818690b25463efe8ab",
"90465da65f6347c9ba4036984b463f12",
"a507db16ab294b578b4093484db153e6",
"148209e484be49cebe036dbc1aebc036",
"5fa3c7df7834429ca8fbe3aa43dcc58b",
"c959aa18431646f39b4a6b189b7a0e2f",
"89dbac7c823448c98131b2f163563f72",
"e71f35380cbd40cea4e2f48eddeb41cc"
]
},
"id": "0sKUSXAh0uIN",
"outputId": "1ce58b63-6067-4180-b88f-ac6e24bbe82d"
},
"source": [
"torch_dev_balanced_tokenized = torch_dev_balanced.map(\n",
" lambda x: tokenizer(x[\"text\"], truncation=True), batched=True, remove_columns=['text']\n",
").map(lambda x: {'label': [all_labels.index(xl) for xl in x['label']]}, batched=True)\n",
"\n",
"torch_dev_balanced_loader = DataLoader(\n",
" torch_dev_balanced_tokenized, \n",
" batch_size=64, drop_last=False, shuffle=False, num_workers=0, collate_fn=data_collator\n",
")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "37596a0a5207491a9820f377e26614d0",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/2 [00:00<?, ?ba/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a5465269d8674da39588a776efe37280",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/2 [00:00<?, ?ba/s]"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145,
"referenced_widgets": [
"ed8b7c27336244c2b771381ac88a5f66",
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"688cbeb12294402896abf8f415428a81",
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"b1ff9ed1b7e64339979971f0fe2b88ab",
"07776e5d08544fcda16c90be8828bc2f",
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"b4b56bf76afe412da7c9c3baab3805e4",
"bc2fd00e79b5409eaf944e0306b98830",
"2d1fda30ce20406784d47f2552f208c9",
"c9a62d1453a8417c85889dddbb62bec8",
"364909cef92d4a5d978ba9903ef55bd3",
"7da13921a44c4de8aaa125d7b630f46d",
"4b20db932791445dafae864d7fbe06dc",
"cd0a736413c342eaba8064560de6edeb",
"1cc7bf80edc54662867d0563bea137a5",
"b0acac64bd9d4712a5c488dd6a8600cf",
"571567493d564f2384cdd62f486f20ea",
"a8b1ac0e5464422da10f8622e234c17f",
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]
},
"id": "FBhZsICu8Yf_",
"outputId": "b2da7c8a-5244-4994-d173-f96fe9a68be2"
},
"source": [
"data_tokenized = torch_data.map(\n",
" lambda x: tokenizer(x[\"text\"], truncation=True), batched=True, remove_columns=['text']\n",
")\n",
"data_tokenized = data_tokenized.map(lambda x: {'label': [all_labels.index(xl) for xl in x['label']]}, batched=True)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ed8b7c27336244c2b771381ac88a5f66",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/549 [00:00<?, ?ba/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6145ec93464b4a44adbc5531d58bd750",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/69 [00:00<?, ?ba/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d5d43924f37d4303a5587b2237353462",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/549 [00:00<?, ?ba/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7da13921a44c4de8aaa125d7b630f46d",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/69 [00:00<?, ?ba/s]"
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},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z9_-1F0ZvQb6"
},
"source": [
"# The first model: unbalanced data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "aqXBfdH2h1QB"
},
"source": [
"batch_size = 64\n",
"\n",
"train_dataloader = DataLoader(\n",
" data_tokenized['train'], \n",
" batch_size=batch_size, drop_last=False, shuffle=True, num_workers=0, collate_fn=data_collator\n",
")\n",
"dev_dataloader = DataLoader(\n",
" data_tokenized['dev'], \n",
" batch_size=batch_size, drop_last=False, shuffle=True, num_workers=0, collate_fn=data_collator\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 240,
"referenced_widgets": [
"00202c8470f94a8f8a9e53b4e48dbac3",
"89c2992dc0bd429db7c04d2868e3eb29",
"ba0aedf50b734fa1a87dc97ca933b91d",
"a117018277544f43b575b4dd0b86ced3",
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"92139d9d223d4bda8bd9e539d990aa95",
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"e8bc197914364e5c8fb7bbc44631d00f",
"ca1f6cc875cf4b1ebdd53b102be71552"
]
},
"id": "g3g_9uRY-cl2",
"outputId": "5a56d6fb-adf7-4a82-9f94-ba15b8009a97"
},
"source": [
"evaluate_model(model, dev_dataloader, verbose=True)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "00202c8470f94a8f8a9e53b4e48dbac3",
"version_minor": 0,
"version_major": 2
},
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" 0%| | 0/1074 [00:00<?, ?it/s]"
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},
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},
{
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" <td>36412.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.235640</td>\n",
" <td>0.202923</td>\n",
" <td>0.218061</td>\n",
" <td>16011.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.391306</td>\n",
" <td>0.387911</td>\n",
" <td>0.381087</td>\n",
" <td>22892.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s\n",
"0 0.267435 0.421399 0.327210 16253.0\n",
"1 0.670845 0.539410 0.597991 36412.0\n",
"2 0.235640 0.202923 0.218061 16011.0\n",
"3 0.391306 0.387911 0.381087 22892.0"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"aucs: [0.3868994199141944, 0.6943497260285278, 0.578275696874899] 0.5531749476058737\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.5531749476058737"
]
},
"metadata": {},
"execution_count": 98
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AmBnFp_I0X8c"
},
"source": [
"А вот после этого момента можно сразу переходить ко второй версии модели"
]
},
{
"cell_type": "code",
"metadata": {
"id": "jswOaxxKsQfr"
},
"source": [
"optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-5)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3eIAmun6m8iB"
},
"source": [
"gradient_accumulation_steps = 1\n",
"window = 500\n",
"cleanup_step = 100\n",
"report_step = 10000"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NGP2xV-GBXIz"
},
"source": [
"ewm_loss = 0"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 368,
"referenced_widgets": [
"4ad60eef52794b8bb4802f175c3740d2",
"03fd5a78ff3048c09c479fe5cdc18ea4",
"6f03bcdca6b84972aab935b0f2cf19bb",
"1d3a7bbf55154b359c0e9ebf28e9e7da",
"18bc7bef1dbc4c2b8a5e8d865da8a8ca",
"17b8f8cc1ef44f28bfba339a2a63806c",
"b8a6628b95ac47678504c84117144d0c",
"cdb685c6a1c04933a735589aee7b25df",
"cb72295819e44563a3876511d7c891df",
"f669e2dd12464ec0a44998a28f752639",
"dde601ad4d9f47dd8f0b16a0b9b1aa9d",
"26c4f0583c2742cc8e384be34cdfd661",
"91441adb65a64ea599eadd4b7f1fc5d7",
"2ea693af356a4592b0110120481f7095",
"7abba4b9edb14560a6ecf98cb88c14b4",
"25373f844632441d854466c30922f6c4",
"43ea430ca6614108b26fba5f55a1a577",
"06536b50d5324b67ab088586fc48ce87",
"d68ae1b72b774e09bd02e9faf6611dbf",
"7d0677b50b63407e9e5e4204d611f3b1",
"dbf8333557394c40a95484f66d21f3b3",
"d7192a18ba134debb92d751cf428a995",
"75541e0a5a4247f69c688a55c34651c0",
"84e212e2a270427facc8192a1805e8e7",
"47d3b07752cb4760932da11f9be9bca4",
"b4d8e88ab8094db2b5e669caea48def4",
"62239f48c4934be6b983e14e297adfcf",
"be96d01c8da544e381c28bdb2a008d93",
"7bed4f1fdca046f198af25807f6350c8",
"23c8a55ba25b498984c728838a0d0b89",
"46aa1a9e69e741e9b07ae7a3d026a022",
"d3fc7a7aa83c498e8a32770e9b89f97d",
"f3c7b4b407a54b7180623c835c3981ba",
"924892e697cc47519e62c356a9d176b4",
"cdc4881659474cf7a0539348315d894e",
"48975e657a00413da9aed87cfad6ed58",
"67581d12ea084ee188fdd73a1501b5a2",
"76128d715d784f85b6e9e256caf78ac5",
"79a8afd213234c429b8f92b178ab04ef",
"882cd1c4896646819ac1cd84e76707a1",
"a8c5115033744f3c9c9b89c2ee130746",
"372986ff7607412492c8ce438da25b64",
"1b98e3e95d044446a87980c435b1e399",
"b4c49de51dea4ff1a38d9fd3c7e73a03",
"14ec820bb4cb4dfa89cf6cae5f304b44",
"6f1d10593c414f8489cc541d22d857bc",
"710b10243f4d44dcb988cd7dcec1628a",
"bc2070fb404242fd9cf12ef6dab3fab0",
"a981ed12ed6a43eb8b2f853a1bd4e55b",
"5b849f95823642c690a1950c0399964c",
"8f5be09aac85406b8491fbd0c8d5cc75",
"74d1811467d24137b93108c26f0502ea",
"36ba7bf4a3344d1583196059b7bbc3f5",
"6a9c8ce5651b495095e0d7643b3eaa5b",
"3dcde3fdf8c04f7ab5cedf6c47792d6d"
]
},
"id": "21O_LRSHm2Nm",
"outputId": "c23dd860-1ad1-4fd1-cfc3-d35c955ba156"
},
"source": [
"model.train()\n",
"cleanup()\n",
"\n",
"for epoch in trange(3):\n",
" tq = tqdm(train_dataloader)\n",
" \n",
" for i, batch in enumerate(tq):\n",
" try:\n",
" batch = batch.to(model.device)\n",
" output = model(**batch)\n",
" loss = output.loss\n",
" loss.backward()\n",
" except RuntimeError as e:\n",
" print('error on step', i, e)\n",
" loss = None\n",
" cleanup()\n",
" continue\n",
"\n",
" if i and i % gradient_accumulation_steps == 0:\n",
" optimizer.step()\n",
" optimizer.zero_grad()\n",
" \n",
" if i % cleanup_step == 0:\n",
" cleanup()\n",
"\n",
" w = 1 / min(i+1, window)\n",
" ewm_loss = ewm_loss * (1-w) + loss.item() * w\n",
" tq.set_description(f'loss: {ewm_loss:4.4f}')\n",
" \n",
" if i % report_step == 0:\n",
" model.eval()\n",
" eval_loss = evaluate_model(model, dev_dataloader, verbose=True)\n",
" model.train()\n",
" print(f'epoch {epoch}, step {i}: train loss: {ewm_loss:4.4f} val auc: {eval_loss}')\n",
"\n",
"model.eval()\n",
"eval_loss = evaluate_model(model, dev_dataloader, verbose=True)\n",
"print(f'epoch {epoch + 1}, step {i}: train loss: {ewm_loss:4.4f} val auc: {eval_loss}')"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4ad60eef52794b8bb4802f175c3740d2",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "26c4f0583c2742cc8e384be34cdfd661",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/8570 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "75541e0a5a4247f69c688a55c34651c0",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/8570 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "924892e697cc47519e62c356a9d176b4",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/8570 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "14ec820bb4cb4dfa89cf6cae5f304b44",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/1074 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>p</th>\n",
" <th>r</th>\n",
" <th>f</th>\n",
" <th>s</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.921001</td>\n",
" <td>0.869378</td>\n",
" <td>0.894445</td>\n",
" <td>16253.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.921058</td>\n",
" <td>0.948149</td>\n",
" <td>0.934407</td>\n",
" <td>36412.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.939688</td>\n",
" <td>0.930298</td>\n",
" <td>0.934970</td>\n",
" <td>16011.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.927249</td>\n",
" <td>0.915942</td>\n",
" <td>0.921274</td>\n",
" <td>22892.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s\n",
"0 0.921001 0.869378 0.894445 16253.0\n",
"1 0.921058 0.948149 0.934407 36412.0\n",
"2 0.939688 0.930298 0.934970 16011.0\n",
"3 0.927249 0.915942 0.921274 22892.0"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"aucs: [0.9895661727076159, 0.9864192978440336, 0.9935832239563914] 0.9898562315026803\n",
"epoch 3, step 8569: train loss: 0.1743 val auc: 0.9898562315026803\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jHqRhX48Y_AH",
"outputId": "305944ff-abb8-4b1e-f35e-ec8d15d21e9d"
},
"source": [
"NEW_MODEL_NAME = '/gd/MyDrive/models/rubert-tiny-sentiment'\n",
"model.save_pretrained(NEW_MODEL_NAME)\n",
"tokenizer.save_pretrained(NEW_MODEL_NAME)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('/gd/MyDrive/models/rubert-tiny-sentiment/tokenizer_config.json',\n",
" '/gd/MyDrive/models/rubert-tiny-sentiment/special_tokens_map.json',\n",
" '/gd/MyDrive/models/rubert-tiny-sentiment/vocab.txt',\n",
" '/gd/MyDrive/models/rubert-tiny-sentiment/added_tokens.json',\n",
" '/gd/MyDrive/models/rubert-tiny-sentiment/tokenizer.json')"
]
},
"metadata": {},
"execution_count": 109
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ory4ZiwttqSN"
},
"source": [
"# Evaluation"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 221
},
"id": "WcwuVZ6Etsqe",
"outputId": "720f76cf-0684-4e70-c04f-18053a57f1a8"
},
"source": [
"dev_dataloader_fixed = DataLoader(\n",
" data_tokenized['dev'], \n",
" batch_size=32, drop_last=False, shuffle=False, num_workers=0, collate_fn=data_collator\n",
")"
],
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-9c1330320147>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m dev_dataloader_fixed = DataLoader(\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdata_tokenized\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dev'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_last\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollate_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_collator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m )\n",
"\u001b[0;31mNameError\u001b[0m: name 'DataLoader' is not defined"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"85b5336383884645914218fb75e33d10",
"efe0464c094b4d2996368786e32a0012",
"b83a342bac9047c1b6f6252287e467ca",
"50ed0719334a41e8b41cde28c4090693",
"5669e5f57ed2486bb9e675db990a930b",
"9d6a650080394671985f4744fc3fca9f",
"27ea406bf46c475dbc8525700f0227a8",
"74320239ac964cd48dcf1660546375b5",
"477c979ece4e4981abfef41239942683",
"a3dc7463249943a6a2668f3a280bbf2f",
"d6733bba23f34aeba9a5e8e834aa149c"
]
},
"id": "34-yfi3CuSrP",
"outputId": "b448eb3b-3f22-4b8a-d440-3c672a9fb839"
},
"source": [
"preds = []\n",
"facts = []\n",
"\n",
"for batch in tqdm(dev_dataloader_fixed):\n",
" facts.append(batch.labels.cpu().numpy())\n",
" batch = batch.to(model.device)\n",
" with torch.no_grad():\n",
" pr = model(input_ids=batch.input_ids, attention_mask=batch.attention_mask, token_type_ids=batch.token_type_ids)\n",
" preds.append(torch.softmax(pr.logits, -1).cpu().numpy())\n",
"facts = np.concatenate(facts)\n",
"preds = np.concatenate(preds)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "85b5336383884645914218fb75e33d10",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/1074 [00:00<?, ?it/s]"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "j5nLo4hauZso",
"outputId": "58e4d46c-be35-41ce-e294-2ca4ff11bb92"
},
"source": [
"dev = full_sentiment_data[full_sentiment_data.split=='dev'].copy()\n",
"print(dev.shape)\n",
"assert (dev.label == [all_labels[i] for i in facts]).all()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(68676, 4)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xbGLbEdJurYw"
},
"source": [
"dev['preds'] = [all_labels[i] for i in preds.argmax(axis=1)]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "I5MKc3Zau0Il"
},
"source": [
"from sklearn.metrics import accuracy_score, f1_score"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
},
"id": "eedFARdhvCyT",
"outputId": "920412cc-0357-4e27-86f9-f637816da6b4"
},
"source": [
"dev.groupby('source').apply(lambda x: pd.Series({\n",
" 'n': x.shape[0],\n",
" 'accuracy': accuracy_score(x.label, x.preds), \n",
" 'f1_macro': f1_score(x.label, x.preds, average='macro')\n",
"}))"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n</th>\n",
" <th>accuracy</th>\n",
" <th>f1_macro</th>\n",
" </tr>\n",
" <tr>\n",
" <th>source</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>SentiRuEval2016_banks</th>\n",
" <td>1055.0</td>\n",
" <td>0.869194</td>\n",
" <td>0.709848</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SentiRuEval2016_tele</th>\n",
" <td>937.0</td>\n",
" <td>0.707577</td>\n",
" <td>0.634077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>kaggle_news</th>\n",
" <td>840.0</td>\n",
" <td>0.651190</td>\n",
" <td>0.604491</td>\n",
" </tr>\n",
" <tr>\n",
" <th>linis</th>\n",
" <td>2675.0</td>\n",
" <td>0.601495</td>\n",
" <td>0.525590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mokoron</th>\n",
" <td>51886.0</td>\n",
" <td>0.996203</td>\n",
" <td>0.996019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rureviews</th>\n",
" <td>9171.0</td>\n",
" <td>0.737651</td>\n",
" <td>0.740745</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rusentiment</th>\n",
" <td>2112.0</td>\n",
" <td>0.643466</td>\n",
" <td>0.549202</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n accuracy f1_macro\n",
"source \n",
"SentiRuEval2016_banks 1055.0 0.869194 0.709848\n",
"SentiRuEval2016_tele 937.0 0.707577 0.634077\n",
"kaggle_news 840.0 0.651190 0.604491\n",
"linis 2675.0 0.601495 0.525590\n",
"mokoron 51886.0 0.996203 0.996019\n",
"rureviews 9171.0 0.737651 0.740745\n",
"rusentiment 2112.0 0.643466 0.549202"
]
},
"metadata": {},
"execution_count": 128
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kmWlV0lBvjQJ"
},
"source": [
"Мораль: модель, обученная на очень несбалансированном датасете, подстраивается под свою наиболее массивную часть. Нужно попробовать пересэмплировать всё нафиг. "
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
},
"id": "WN-PdSsxwIgm",
"outputId": "82cf45f5-4a49-473c-82f4-6e1032eeea01"
},
"source": [
"resampled = dev.groupby(['source', 'label']).sample(70, random_state=1).groupby('source').apply(lambda x: pd.Series({\n",
" 'n': x.shape[0],\n",
" 'accuracy': accuracy_score(x.label, x.preds), \n",
" 'f1_macro': f1_score(x.label, x.preds, average='macro')\n",
"}))\n",
"resampled"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n</th>\n",
" <th>accuracy</th>\n",
" <th>f1_macro</th>\n",
" </tr>\n",
" <tr>\n",
" <th>source</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>SentiRuEval2016_banks</th>\n",
" <td>210.0</td>\n",
" <td>0.676190</td>\n",
" <td>0.643551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SentiRuEval2016_tele</th>\n",
" <td>210.0</td>\n",
" <td>0.585714</td>\n",
" <td>0.574823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>kaggle_news</th>\n",
" <td>210.0</td>\n",
" <td>0.552381</td>\n",
" <td>0.539622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>linis</th>\n",
" <td>210.0</td>\n",
" <td>0.480952</td>\n",
" <td>0.460348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mokoron</th>\n",
" <td>210.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rureviews</th>\n",
" <td>210.0</td>\n",
" <td>0.738095</td>\n",
" <td>0.740806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rusentiment</th>\n",
" <td>210.0</td>\n",
" <td>0.566667</td>\n",
" <td>0.538639</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n accuracy f1_macro\n",
"source \n",
"SentiRuEval2016_banks 210.0 0.676190 0.643551\n",
"SentiRuEval2016_tele 210.0 0.585714 0.574823\n",
"kaggle_news 210.0 0.552381 0.539622\n",
"linis 210.0 0.480952 0.460348\n",
"mokoron 210.0 1.000000 1.000000\n",
"rureviews 210.0 0.738095 0.740806\n",
"rusentiment 210.0 0.566667 0.538639"
]
},
"metadata": {},
"execution_count": 134
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aeJzIFAuwjK7",
"outputId": "b5e8cd1d-0cd6-4584-c478-c2374512aaf3"
},
"source": [
"resampled.mean()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"n 210.000000\n",
"accuracy 0.657143\n",
"f1_macro 0.642541\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 135
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hvl26gJswt0H"
},
"source": [
"# The second model, resampled and balanced"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "H2xR3bkBxrms",
"outputId": "78d2595f-7125-4a9e-82b3-ad3998c04d62"
},
"source": [
"train_size = sum(full_sentiment_data.split=='train')\n",
"print(train_size)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"548424\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 141
},
"id": "5M3Nl916x0Ur",
"outputId": "610a307f-ce60-407f-f54a-887d314f63e2"
},
"source": [
"full_sentiment_data.sample(3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>text</th>\n",
" <th>label</th>\n",
" <th>source</th>\n",
" <th>split</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>842927</th>\n",
" <td>Нет человека — нет проблем. (с) Сталин.</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1479419</th>\n",
" <td>@xerepacid, #мысли Любые перемены несут с собо...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>817467</th>\n",
" <td>Нет человека — нет проблем. (с) Сталин.</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text label source split\n",
"842927 Нет человека — нет проблем. (с) Сталин. NaN NaN NaN\n",
"1479419 @xerepacid, #мысли Любые перемены несут с собо... NaN NaN NaN\n",
"817467 Нет человека — нет проблем. (с) Сталин. NaN NaN NaN"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YUZjE_OKxaJg"
},
"source": [
"train = {\n",
" k: v.text.tolist()\n",
" for k, v in full_sentiment_data[full_sentiment_data.split=='train'].dropna().groupby(['source', 'label'])\n",
"}"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pu9StbHRx5MI",
"outputId": "5d6f8343-780a-4fb5-c889-86a270ca40a7"
},
"source": [
"print(len(train))\n",
"train.keys()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"21\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict_keys([('SentiRuEval2016_banks', 'negative'), ('SentiRuEval2016_banks', 'neutral'), ('SentiRuEval2016_banks', 'positive'), ('SentiRuEval2016_tele', 'negative'), ('SentiRuEval2016_tele', 'neutral'), ('SentiRuEval2016_tele', 'positive'), ('kaggle_news', 'negative'), ('kaggle_news', 'neutral'), ('kaggle_news', 'positive'), ('linis', 'negative'), ('linis', 'neutral'), ('linis', 'positive'), ('mokoron', 'negative'), ('mokoron', 'neutral'), ('mokoron', 'positive'), ('rureviews', 'negative'), ('rureviews', 'neutral'), ('rureviews', 'positive'), ('rusentiment', 'negative'), ('rusentiment', 'neutral'), ('rusentiment', 'positive')])"
]
},
"metadata": {},
"execution_count": 65
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EIixFVnCx_iA",
"outputId": "6444133a-0659-4cc7-bec3-bcc834a8bcb6"
},
"source": [
"import random\n",
"\n",
"train_keys = list(train.keys())\n",
"\n",
"def sample_batch(n=64):\n",
" texts = []\n",
" labels = []\n",
" for i in range(n):\n",
" source, label = random.choice(train_keys)\n",
" texts.append(random.choice(train[(source, label)]))\n",
" labels.append(all_labels.index(label))\n",
" return texts, labels\n",
"\n",
"sample_batch(2)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(['ждала юбку почти три месяца, так и не пришла. деньги вернули через спор .',\n",
" 'хороший дядька был. Почитал, вроде всё правильно делал... Но процент бедности так и остался довольно высок(. Бунтарь... Уго Чавес...Солдат... КомандантеСегодня покинул мир...И ангелы тихо поют сарабандуА дьяволы прут на пир...Дикарь... настоящий... народный индеец(Он коку жевал всю жизнь...)И верил , свободы текила и перец -Лекарство мужицких жил...И горечь матэ заливая в глотку Тянул бечевой Страну...А в русских просторах морозной водкойИ баней лечил хандру...Чем громче фавелы ему молилисьСкандируя: Уго -Любовь! Тем тише шептались Гудзон с Пикадилли...Нефть – вурдалачья кровь ... В морях озверело гребут шаланды ...Булькает вязкий груз ...Но ламу ведет перевалом в АндыИндейский шаман Иисус ...http://stihi.ru/2013/03/06/12096 и это при всей его биографии .. вы же не знаете ЧТО там было до него.Я работала в Венесуэле с 97 по 2002 годы.Там нищета была 90% населения.Каждый год в газетах давали статистику по доходам, деля население по буквам А, В, С и D в зависимости от доходов.Таки вот, с моей нормальненькой з/п я с моим мужем, работая обыкновенными инженерами на строительстве ГЭС, попадали в уровень под буквой А - это были 2% самую богатую прослойку населения .'],\n",
" [0, 1])"
]
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mwriXR66wwSY",
"outputId": "d17bae8b-c09e-42c5-a9a0-ebacdac6e046"
},
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=len(all_labels))\n",
"model.config.id2label = dict(enumerate(all_labels))\n",
"model.config.label2id = {v: k for k, v in model.config.id2label.items()}\n",
"model.cuda();"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at cointegrated/rubert-tiny were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
"- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at cointegrated/rubert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"3c3bbd9822134f2e915e56b421575ede",
"7e4726d5e0054af9a8e955350bc89762",
"64dcc3d1c0db43c7a44f1dc594cc5006",
"bba8ef56bc074fbc8aa591c72afcbf5c",
"6338b4071a4e487b9749ea024949732b",
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"6202066ebe6d4664bd25b01a953a8573",
"323ec5a2525d4901b83a867fe1ab9927"
]
},
"id": "EUAiM1-u1sIS",
"outputId": "d5570350-4c1a-46d7-9f3e-faffb494ae95"
},
"source": [
""
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c3bbd9822134f2e915e56b421575ede",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/2 [00:00<?, ?ba/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "eb006b25287c467c81fefd6c0820aa66",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/2 [00:00<?, ?ba/s]"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "RMMnyhbI1zLd"
},
"source": [
"torch_dev_balanced_tokenized = torch_dev_balanced.map(\n",
" lambda x: tokenizer(x[\"text\"], truncation=True), batched=True, remove_columns=['text']\n",
").map(lambda x: {'label': [all_labels.index(xl) for xl in x['label']]}, batched=True)\n",
"\n",
"torch_dev_balanced_loader = DataLoader(\n",
" torch_dev_balanced_tokenized, \n",
" batch_size=64, drop_last=False, shuffle=True, num_workers=0, collate_fn=data_collator\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 240,
"referenced_widgets": [
"746d7e58af4548cabd8a6d998a17ed44",
"c342e051880646d7af69f1805f969571",
"d0fd165ea68d46709ae7cb52fd7fea52",
"5162dc6c6848496d8aac1217e482f63b",
"3bcd21b7f25341febee83bfc4e4d6fd6",
"7a93b6babd60443f96a6259b7f27eb6e",
"f7a152bfde0f4c278355befcdcec079f",
"3fae86d28c074716bbfcc65e24a97406",
"9d98166e40c64002abe132832771fff6",
"d8e87506839542fe8f688755a9a941e7",
"ef73b4803a9f4d539169120010821b66"
]
},
"id": "n47UUnnR159H",
"outputId": "e55dc3e2-3047-4d43-fa62-2421aebc38ce"
},
"source": [
"evaluate_model(model, torch_dev_balanced_loader, verbose=True, labels=all_labels)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "746d7e58af4548cabd8a6d998a17ed44",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>p</th>\n",
" <th>r</th>\n",
" <th>f</th>\n",
" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.283967</td>\n",
" <td>0.426531</td>\n",
" <td>0.340946</td>\n",
" <td>490.0</td>\n",
" <td>0.402788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.415385</td>\n",
" <td>0.110204</td>\n",
" <td>0.174194</td>\n",
" <td>490.0</td>\n",
" <td>0.497675</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.298013</td>\n",
" <td>0.367347</td>\n",
" <td>0.329068</td>\n",
" <td>490.0</td>\n",
" <td>0.488076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.332455</td>\n",
" <td>0.301361</td>\n",
" <td>0.281402</td>\n",
" <td>490.0</td>\n",
" <td>0.462846</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.283967 0.426531 0.340946 490.0 0.402788\n",
"neutral 0.415385 0.110204 0.174194 490.0 0.497675\n",
"positive 0.298013 0.367347 0.329068 490.0 0.488076\n",
"mean 0.332455 0.301361 0.281402 490.0 0.462846"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"aucs: [0.40278842149104543, 0.49767492711370265, 0.4880758017492712] 0.4628463834513397\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.4628463834513397"
]
},
"metadata": {},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "T6WmJR-5xZZO"
},
"source": [
"optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-5)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7pdLEa5fy1al"
},
"source": [
"cleanup()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6WNv4dVVy6Ix"
},
"source": [
"gradient_accumulation_steps = 1\n",
"window = 500\n",
"cleanup_step = 100\n",
"report_step = 3000"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "GeyKegYly-QE"
},
"source": [
"ewm_loss = 0"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2FL4_gDY25IY"
},
"source": [
"batch_size = 64"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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"d8ab8e8253d54f96becb45c7ff2887da",
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"e30837cd91b64566b9fd1bae3bf63fba",
"38898f6c3f9b48a986c36ec50922615c",
"ab23ccfc848e47e189b0526d314bc477",
"828c48a950a24cad910a98c2dca82430",
"7e98e955f4df408d9f48d95686153c3e",
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]
},
"id": "rn3pogvVy_lv",
"outputId": "024da89d-e943-45ac-a906-cc905111c3d6"
},
"source": [
"model.train()\n",
"cleanup()\n",
"\n",
"tq = trange(int(train_size / batch_size * 3)) # 3 примерные эпохи\n",
"\n",
"for i in tq:\n",
" try:\n",
" texts, labels = sample_batch(n=batch_size)\n",
" inputs = tokenizer(texts, truncation=True, padding=True, return_tensors='pt').to(model.device)\n",
" output = model(labels=torch.tensor(labels).to(model.device), **inputs)\n",
" loss = output.loss\n",
" loss.backward()\n",
" except RuntimeError as e:\n",
" print('error on step', i, e)\n",
" loss = None\n",
" cleanup()\n",
" continue\n",
"\n",
" if i and i % gradient_accumulation_steps == 0:\n",
" optimizer.step()\n",
" optimizer.zero_grad()\n",
" \n",
" if i % cleanup_step == 0:\n",
" cleanup()\n",
"\n",
" w = 1 / min(i+1, window)\n",
" ewm_loss = ewm_loss * (1-w) + loss.item() * w\n",
" tq.set_description(f'loss: {ewm_loss:4.4f}')\n",
" \n",
" if i and i % report_step == 0:\n",
" cleanup()\n",
" model.eval()\n",
" eval_loss = evaluate_model(model, torch_dev_balanced_loader, verbose=True, labels=all_labels)\n",
" model.train()\n",
" print(f'step {i}: train loss: {ewm_loss:4.4f} val auc: {eval_loss}')\n",
" cleanup()\n",
"\n",
"model.eval()\n",
"eval_loss = evaluate_model(model, torch_dev_balanced_loader, verbose=True, labels=all_labels)\n",
"print(f'step {i}: train loss: {ewm_loss:4.4f} val auc: {eval_loss}')"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
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"model_id": "d8ab8e8253d54f96becb45c7ff2887da",
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"version_major": 2
},
"text/plain": [
" 0%| | 0/25707 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "102ab39fa45c41aaae4c515e39740c08",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>p</th>\n",
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" <th>f</th>\n",
" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.728778</td>\n",
" <td>0.718367</td>\n",
" <td>0.723535</td>\n",
" <td>490.0</td>\n",
" <td>0.900676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.650108</td>\n",
" <td>0.614286</td>\n",
" <td>0.631689</td>\n",
" <td>490.0</td>\n",
" <td>0.825878</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.723282</td>\n",
" <td>0.773469</td>\n",
" <td>0.747535</td>\n",
" <td>490.0</td>\n",
" <td>0.894082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.700723</td>\n",
" <td>0.702041</td>\n",
" <td>0.700920</td>\n",
" <td>490.0</td>\n",
" <td>0.873545</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.728778 0.718367 0.723535 490.0 0.900676\n",
"neutral 0.650108 0.614286 0.631689 490.0 0.825878\n",
"positive 0.723282 0.773469 0.747535 490.0 0.894082\n",
"mean 0.700723 0.702041 0.700920 490.0 0.873545"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"aucs: [0.9006757600999582, 0.8258777592669722, 0.8940816326530613] 0.8735450506733305\n",
"step 3000: train loss: 0.6469 val auc: 0.8735450506733305\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6c18aa4c5a1b4ccd96588db410a6bb3d",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.744681</td>\n",
" <td>0.714286</td>\n",
" <td>0.729167</td>\n",
" <td>490.0</td>\n",
" <td>0.908183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.647773</td>\n",
" <td>0.653061</td>\n",
" <td>0.650407</td>\n",
" <td>490.0</td>\n",
" <td>0.838877</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.737154</td>\n",
" <td>0.761224</td>\n",
" <td>0.748996</td>\n",
" <td>490.0</td>\n",
" <td>0.903807</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.709869</td>\n",
" <td>0.709524</td>\n",
" <td>0.709523</td>\n",
" <td>490.0</td>\n",
" <td>0.883622</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.744681 0.714286 0.729167 490.0 0.908183\n",
"neutral 0.647773 0.653061 0.650407 490.0 0.838877\n",
"positive 0.737154 0.761224 0.748996 490.0 0.903807\n",
"mean 0.709869 0.709524 0.709523 490.0 0.883622"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"aucs: [0.908183048729696, 0.8388765097875885, 0.9038067471886714] 0.8836221019019853\n",
"step 6000: train loss: 0.5629 val auc: 0.8836221019019853\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4be7380750314d3ab6a9322d9345dcd5",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
]
},
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},
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"output_type": "display_data",
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" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.748441</td>\n",
" <td>0.734694</td>\n",
" <td>0.741504</td>\n",
" <td>490.0</td>\n",
" <td>0.910551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.644269</td>\n",
" <td>0.665306</td>\n",
" <td>0.654618</td>\n",
" <td>490.0</td>\n",
" <td>0.845601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.766046</td>\n",
" <td>0.755102</td>\n",
" <td>0.760534</td>\n",
" <td>490.0</td>\n",
" <td>0.904512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.719585</td>\n",
" <td>0.718367</td>\n",
" <td>0.718886</td>\n",
" <td>490.0</td>\n",
" <td>0.886888</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.748441 0.734694 0.741504 490.0 0.910551\n",
"neutral 0.644269 0.665306 0.654618 490.0 0.845601\n",
"positive 0.766046 0.755102 0.760534 490.0 0.904512\n",
"mean 0.719585 0.718367 0.718886 490.0 0.886888"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"aucs: [0.9105508121615993, 0.845600791336943, 0.9045116618075802] 0.8868877551020408\n",
"step 9000: train loss: 0.5022 val auc: 0.8868877551020408\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "565f4b85d74d4f81872c77438a81b998",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
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" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.731939</td>\n",
" <td>0.785714</td>\n",
" <td>0.757874</td>\n",
" <td>490.0</td>\n",
" <td>0.913091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.683652</td>\n",
" <td>0.657143</td>\n",
" <td>0.670135</td>\n",
" <td>490.0</td>\n",
" <td>0.852902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.773784</td>\n",
" <td>0.746939</td>\n",
" <td>0.760125</td>\n",
" <td>490.0</td>\n",
" <td>0.903426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.729792</td>\n",
" <td>0.729932</td>\n",
" <td>0.729378</td>\n",
" <td>490.0</td>\n",
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"text/plain": [
" p r f s a\n",
"negative 0.731939 0.785714 0.757874 490.0 0.913091\n",
"neutral 0.683652 0.657143 0.670135 490.0 0.852902\n",
"positive 0.773784 0.746939 0.760125 490.0 0.903426\n",
"mean 0.729792 0.729932 0.729378 490.0 0.889806"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"aucs: [0.9130914202415661, 0.8529019158683881, 0.9034256559766765] 0.8898063306955436\n",
"step 12000: train loss: 0.4585 val auc: 0.8898063306955436\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
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"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.733333</td>\n",
" <td>0.785714</td>\n",
" <td>0.758621</td>\n",
" <td>490.0</td>\n",
" <td>0.914041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.681529</td>\n",
" <td>0.655102</td>\n",
" <td>0.668054</td>\n",
" <td>490.0</td>\n",
" <td>0.854757</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.763713</td>\n",
" <td>0.738776</td>\n",
" <td>0.751037</td>\n",
" <td>490.0</td>\n",
" <td>0.900154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.726192</td>\n",
" <td>0.726531</td>\n",
" <td>0.725904</td>\n",
" <td>490.0</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.733333 0.785714 0.758621 490.0 0.914041\n",
"neutral 0.681529 0.655102 0.668054 490.0 0.854757\n",
"positive 0.763713 0.738776 0.751037 490.0 0.900154\n",
"mean 0.726192 0.726531 0.725904 490.0 0.889651"
]
},
"metadata": {}
},
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"output_type": "stream",
"text": [
"aucs: [0.9140410245730947, 0.8547573927530195, 0.9001541024573095] 0.8896508399278079\n",
"step 15000: train loss: 0.4233 val auc: 0.8896508399278079\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8804176e7d4f46e6859fa521c21903f4",
"version_minor": 0,
"version_major": 2
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"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
]
},
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"output_type": "display_data",
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" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.763265</td>\n",
" <td>0.763265</td>\n",
" <td>0.763265</td>\n",
" <td>490.0</td>\n",
" <td>0.909412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.670732</td>\n",
" <td>0.673469</td>\n",
" <td>0.672098</td>\n",
" <td>490.0</td>\n",
" <td>0.851519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.762295</td>\n",
" <td>0.759184</td>\n",
" <td>0.760736</td>\n",
" <td>490.0</td>\n",
" <td>0.899475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.732097</td>\n",
" <td>0.731973</td>\n",
" <td>0.732033</td>\n",
" <td>490.0</td>\n",
" <td>0.886802</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.763265 0.763265 0.763265 490.0 0.909412\n",
"neutral 0.670732 0.673469 0.672098 490.0 0.851519\n",
"positive 0.762295 0.759184 0.760736 490.0 0.899475\n",
"mean 0.732097 0.731973 0.732033 490.0 0.886802"
]
},
"metadata": {}
},
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"output_type": "stream",
"text": [
"aucs: [0.909411703456893, 0.8515191586838817, 0.8994752186588921] 0.8868020269332223\n",
"step 18000: train loss: 0.3871 val auc: 0.8868020269332223\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2de245950b2a4373a3d4d4aec96b30e4",
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"version_major": 2
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"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
]
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" <th>s</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.759596</td>\n",
" <td>0.767347</td>\n",
" <td>0.763452</td>\n",
" <td>490.0</td>\n",
" <td>0.912465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.662745</td>\n",
" <td>0.689796</td>\n",
" <td>0.676000</td>\n",
" <td>490.0</td>\n",
" <td>0.854447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.774194</td>\n",
" <td>0.734694</td>\n",
" <td>0.753927</td>\n",
" <td>490.0</td>\n",
" <td>0.891818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.732178</td>\n",
" <td>0.730612</td>\n",
" <td>0.731126</td>\n",
" <td>490.0</td>\n",
" <td>0.886243</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.759596 0.767347 0.763452 490.0 0.912465\n",
"neutral 0.662745 0.689796 0.676000 490.0 0.854447\n",
"positive 0.774194 0.734694 0.753927 490.0 0.891818\n",
"mean 0.732178 0.730612 0.731126 490.0 0.886243"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"aucs: [0.9124645980841316, 0.8544471053727613, 0.8918179925031237] 0.8862432319866721\n",
"step 21000: train loss: 0.3658 val auc: 0.8862432319866721\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "85c700a413164528be7e0a98ec059ae3",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.778993</td>\n",
" <td>0.726531</td>\n",
" <td>0.751848</td>\n",
" <td>490.0</td>\n",
" <td>0.911355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.650699</td>\n",
" <td>0.665306</td>\n",
" <td>0.657921</td>\n",
" <td>490.0</td>\n",
" <td>0.851336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.736328</td>\n",
" <td>0.769388</td>\n",
" <td>0.752495</td>\n",
" <td>490.0</td>\n",
" <td>0.894698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.722007</td>\n",
" <td>0.720408</td>\n",
" <td>0.720755</td>\n",
" <td>490.0</td>\n",
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"text/plain": [
" p r f s a\n",
"negative 0.778993 0.726531 0.751848 490.0 0.911355\n",
"neutral 0.650699 0.665306 0.657921 490.0 0.851336\n",
"positive 0.736328 0.769388 0.752495 490.0 0.894698\n",
"mean 0.722007 0.720408 0.720755 490.0 0.885796"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"aucs: [0.9113546438983756, 0.851335901707622, 0.8946980424822991] 0.8857961960294323\n",
"step 24000: train loss: 0.3407 val auc: 0.8857961960294323\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bc270ba67ae241ed8194dac59f022d86",
"version_minor": 0,
"version_major": 2
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"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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" <th>f</th>\n",
" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.750484</td>\n",
" <td>0.791837</td>\n",
" <td>0.770606</td>\n",
" <td>490.0</td>\n",
" <td>0.912315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.668616</td>\n",
" <td>0.700000</td>\n",
" <td>0.683948</td>\n",
" <td>490.0</td>\n",
" <td>0.854437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.793182</td>\n",
" <td>0.712245</td>\n",
" <td>0.750538</td>\n",
" <td>490.0</td>\n",
" <td>0.891624</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.737427</td>\n",
" <td>0.734694</td>\n",
" <td>0.735031</td>\n",
" <td>490.0</td>\n",
" <td>0.886125</td>\n",
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"text/plain": [
" p r f s a\n",
"negative 0.750484 0.791837 0.770606 490.0 0.912315\n",
"neutral 0.668616 0.700000 0.683948 490.0 0.854437\n",
"positive 0.793182 0.712245 0.750538 490.0 0.891624\n",
"mean 0.737427 0.734694 0.735031 490.0 0.886125"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"aucs: [0.9123146605581006, 0.8544366930445648, 0.8916243231986672] 0.8861252256004443\n",
"step 25706: train loss: 0.3305 val auc: 0.8861252256004443\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "92_n8XNP3xUr",
"outputId": "216bb7fc-e949-4d6c-f37b-c29f961b8ecc"
},
"source": [
"NEW_MODEL_NAME = '/gd/MyDrive/models/rubert-tiny-sentiment-balanced'\n",
"model.save_pretrained(NEW_MODEL_NAME)\n",
"tokenizer.save_pretrained(NEW_MODEL_NAME)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('/gd/MyDrive/models/rubert-tiny-sentiment-balanced/tokenizer_config.json',\n",
" '/gd/MyDrive/models/rubert-tiny-sentiment-balanced/special_tokens_map.json',\n",
" '/gd/MyDrive/models/rubert-tiny-sentiment-balanced/vocab.txt',\n",
" '/gd/MyDrive/models/rubert-tiny-sentiment-balanced/added_tokens.json',\n",
" '/gd/MyDrive/models/rubert-tiny-sentiment-balanced/tokenizer.json')"
]
},
"metadata": {},
"execution_count": 68
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eeaQA-l1iDR5"
},
"source": [
"# Compare with baselines"
]
},
{
"cell_type": "code",
"metadata": {
"id": "q7hOGKf2iFdu"
},
"source": [
"model1 = AutoModelForSequenceClassification.from_pretrained('/gd/MyDrive/models/rubert-tiny-sentiment').cuda();\n",
"model2 = AutoModelForSequenceClassification.from_pretrained('/gd/MyDrive/models/rubert-tiny-sentiment-balanced').cuda();"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
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"ae57110d847c4f7fa96f1f49ee809565",
"fb29df5a68fe4e98a78f9cda904f2b51",
"fbb7337af4b24c1fbb2cb8ae9f3ad6cb",
"d6f3381d227b41f3b80e99f113ecb4aa",
"5cc62d921c7f4c1880a3bad90f4b0944",
"91d3bf95fc0b4c608535cfcc69e2847e",
"465422f9b11e4ba7a51b4a77a44205d4",
"eebdc3ba951d42ac8dc731e30b87bb59",
"1e07aaf9afdd49dc851d542a95157be7",
"c3d9e3621aea41d6a458a7e568097151",
"c3dc5c541c1547b78db8046481eedc4d",
"afe9ed48940b468b9d95ade275166adf",
"46f6595483c842978d149c67151d3346",
"447cb506faa443e3891a314396bb5446",
"d2b4c61bf3354765a4752607ad4d2a0d"
]
},
"id": "BdgpE0UPkSXE",
"outputId": "7107ba7e-2654-4212-d026-e8c7ea0b27b1"
},
"source": [
"torch_dev_balanced_tokenized = torch_dev_balanced.map(\n",
" lambda x: tokenizer(x[\"text\"], truncation=True), batched=True, remove_columns=['text']\n",
").map(lambda x: {'label': [all_labels.index(xl) for xl in x['label']]}, batched=True)\n",
"\n",
"torch_dev_balanced_loader = DataLoader(\n",
" torch_dev_balanced_tokenized, \n",
" batch_size=64, drop_last=False, shuffle=True, num_workers=0, collate_fn=data_collator\n",
")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ca5a8bc6aeea4a7cb0d926bcf707fcbc",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/2 [00:00<?, ?ba/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
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"text/plain": [
" 0%| | 0/2 [00:00<?, ?ba/s]"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 445,
"referenced_widgets": [
"610841528d514af88b4524f1d04d109d",
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},
"id": "oXLpQh0zs0An",
"outputId": "8c38cf45-3188-4863-f123-ccc74a1c61de"
},
"source": [
"evaluate_model(model1, torch_dev_balanced_loader, verbose=True, labels=all_labels)\n",
"evaluate_model(model2, torch_dev_balanced_loader, verbose=True, labels=all_labels)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "610841528d514af88b4524f1d04d109d",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>p</th>\n",
" <th>r</th>\n",
" <th>f</th>\n",
" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.792350</td>\n",
" <td>0.591837</td>\n",
" <td>0.677570</td>\n",
" <td>490.0</td>\n",
" <td>0.882485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.524194</td>\n",
" <td>0.795918</td>\n",
" <td>0.632091</td>\n",
" <td>490.0</td>\n",
" <td>0.813036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.794444</td>\n",
" <td>0.583673</td>\n",
" <td>0.672941</td>\n",
" <td>490.0</td>\n",
" <td>0.882904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.703663</td>\n",
" <td>0.657143</td>\n",
" <td>0.660867</td>\n",
" <td>490.0</td>\n",
" <td>0.859475</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.792350 0.591837 0.677570 490.0 0.882485\n",
"neutral 0.524194 0.795918 0.632091 490.0 0.813036\n",
"positive 0.794444 0.583673 0.672941 490.0 0.882904\n",
"mean 0.703663 0.657143 0.660867 490.0 0.859475"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"aucs: [0.8824854227405248, 0.813036234902124, 0.8829039983340274] 0.859475218658892\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3aabec8cc08241a4b3f6825e756ae545",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>p</th>\n",
" <th>r</th>\n",
" <th>f</th>\n",
" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.738964</td>\n",
" <td>0.785714</td>\n",
" <td>0.761622</td>\n",
" <td>490.0</td>\n",
" <td>0.909920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.654150</td>\n",
" <td>0.675510</td>\n",
" <td>0.664659</td>\n",
" <td>490.0</td>\n",
" <td>0.854236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.801354</td>\n",
" <td>0.724490</td>\n",
" <td>0.760986</td>\n",
" <td>490.0</td>\n",
" <td>0.895053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.731489</td>\n",
" <td>0.728571</td>\n",
" <td>0.729089</td>\n",
" <td>490.0</td>\n",
" <td>0.886403</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.738964 0.785714 0.761622 490.0 0.909920\n",
"neutral 0.654150 0.675510 0.664659 490.0 0.854236\n",
"positive 0.801354 0.724490 0.760986 490.0 0.895053\n",
"mean 0.731489 0.728571 0.729089 490.0 0.886403"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"aucs: [0.9099198250728862, 0.8542357351103707, 0.8950531028738027] 0.8864028876856865\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.8864028876856865"
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RUisnkj8kXnQ"
},
"source": [
"Сбалансированная модель явно лучше. Но как насчёт простого бейзлайна?"
]
},
{
"cell_type": "code",
"metadata": {
"id": "eTGjTYlIkeHC"
},
"source": [
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.feature_extraction.text import HashingVectorizer\n",
"from sklearn.metrics import roc_auc_score, accuracy_score, f1_score"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mpOPbU3hmGUw"
},
"source": [
"dev_balanced['label_id'] = [all_labels.index(xl) for xl in dev_balanced['label']]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5sKDuXPrlJzR"
},
"source": [
"train_small = train_data.sample(100000, random_state=1)\n",
"train_small['label_id'] = [all_labels.index(xl) for xl in train_small['label']]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
},
"id": "PJmirTlik8Zf",
"outputId": "14a270bc-6c5c-44c1-f68d-b970c8c918d8"
},
"source": [
"%%time\n",
"pipe = make_pipeline(\n",
" HashingVectorizer(analyzer='char', n_features=300_000, ngram_range=(3, 6)), \n",
" LogisticRegression(max_iter=1000, solver='saga')\n",
")\n",
"pipe.fit(train_small.text, train_small.label_id)\n",
"display(get_classification_report(dev_balanced.label_id, pipe.predict_proba(dev_balanced.text), labels=all_labels)[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>p</th>\n",
" <th>r</th>\n",
" <th>f</th>\n",
" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.695122</td>\n",
" <td>0.581633</td>\n",
" <td>0.633333</td>\n",
" <td>490.0</td>\n",
" <td>0.849076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.486202</td>\n",
" <td>0.755102</td>\n",
" <td>0.591527</td>\n",
" <td>490.0</td>\n",
" <td>0.758511</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.725753</td>\n",
" <td>0.442857</td>\n",
" <td>0.550063</td>\n",
" <td>490.0</td>\n",
" <td>0.793168</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.635692</td>\n",
" <td>0.593197</td>\n",
" <td>0.591641</td>\n",
" <td>490.0</td>\n",
" <td>0.800252</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.695122 0.581633 0.633333 490.0 0.849076\n",
"neutral 0.486202 0.755102 0.591527 490.0 0.758511\n",
"positive 0.725753 0.442857 0.550063 490.0 0.793168\n",
"mean 0.635692 0.593197 0.591641 490.0 0.800252"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"CPU times: user 1min 12s, sys: 695 ms, total: 1min 13s\n",
"Wall time: 1min 13s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "k8X7hjgdm2al"
},
"source": [
"Balance only classes; this definitely helps. "
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
},
"id": "RrdvpZ_amc92",
"outputId": "9df26cbe-2ed7-4824-f767-a46c2e2af8e8"
},
"source": [
"%%time\n",
"pipe_b1 = make_pipeline(\n",
" HashingVectorizer(analyzer='char', n_features=300_000, ngram_range=(3, 6)), \n",
" LogisticRegression(max_iter=1000, solver='saga', class_weight='balanced')\n",
")\n",
"pipe_b1.fit(train_small.text, train_small.label_id)\n",
"display(get_classification_report(dev_balanced.label_id, pipe_b1.predict_proba(dev_balanced.text), labels=all_labels)[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>p</th>\n",
" <th>r</th>\n",
" <th>f</th>\n",
" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.643510</td>\n",
" <td>0.718367</td>\n",
" <td>0.678881</td>\n",
" <td>490.0</td>\n",
" <td>0.859264</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.568862</td>\n",
" <td>0.581633</td>\n",
" <td>0.575177</td>\n",
" <td>490.0</td>\n",
" <td>0.752591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.691943</td>\n",
" <td>0.595918</td>\n",
" <td>0.640351</td>\n",
" <td>490.0</td>\n",
" <td>0.810263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.634772</td>\n",
" <td>0.631973</td>\n",
" <td>0.631470</td>\n",
" <td>490.0</td>\n",
" <td>0.807373</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.643510 0.718367 0.678881 490.0 0.859264\n",
"neutral 0.568862 0.581633 0.575177 490.0 0.752591\n",
"positive 0.691943 0.595918 0.640351 490.0 0.810263\n",
"mean 0.634772 0.631973 0.631470 490.0 0.807373"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"CPU times: user 1min 18s, sys: 459 ms, total: 1min 19s\n",
"Wall time: 1min 18s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y-6MhXK8n6Qt"
},
"source": [
"What about words? They fare worse"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
},
"id": "4WK144Uan5rp",
"outputId": "bff564e3-a264-4f24-93b3-0d1a1c825284"
},
"source": [
"%%time\n",
"pipe_b1 = make_pipeline(\n",
" HashingVectorizer(analyzer='word', n_features=300_000, ngram_range=(1,2)), \n",
" LogisticRegression(max_iter=1000, solver='saga', class_weight='balanced')\n",
")\n",
"pipe_b1.fit(train_small.text, train_small.label_id)\n",
"display(get_classification_report(dev_balanced.label_id, pipe_b1.predict_proba(dev_balanced.text), labels=all_labels)[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>p</th>\n",
" <th>r</th>\n",
" <th>f</th>\n",
" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.614159</td>\n",
" <td>0.708163</td>\n",
" <td>0.657820</td>\n",
" <td>490.0</td>\n",
" <td>0.832050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.543478</td>\n",
" <td>0.561224</td>\n",
" <td>0.552209</td>\n",
" <td>490.0</td>\n",
" <td>0.728746</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.624060</td>\n",
" <td>0.508163</td>\n",
" <td>0.560180</td>\n",
" <td>490.0</td>\n",
" <td>0.774703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.593899</td>\n",
" <td>0.592517</td>\n",
" <td>0.590070</td>\n",
" <td>490.0</td>\n",
" <td>0.778500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.614159 0.708163 0.657820 490.0 0.832050\n",
"neutral 0.543478 0.561224 0.552209 490.0 0.728746\n",
"positive 0.624060 0.508163 0.560180 490.0 0.774703\n",
"mean 0.593899 0.592517 0.590070 490.0 0.778500"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"CPU times: user 10.1 s, sys: 20.1 ms, total: 10.2 s\n",
"Wall time: 10.1 s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nQGZOuxIm1z_"
},
"source": [
"Balance sources and classes"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "H7s7po6Gm7U7",
"outputId": "71aa2c64-f2bd-4073-bf78-d06ac77f98f2"
},
"source": [
"group_size = int(train_data.shape[0] * 0.1 / train_data.source.nunique() / train_data.label.nunique())\n",
"print(group_size)\n",
"bala_train = train_data.groupby(['source', 'label']).apply(lambda x: x.sample(group_size, random_state=1, replace=(x.shape[0] < group_size)))\n",
"bala_train['label_id'] = [all_labels.index(xl) for xl in bala_train['label']]\n",
"print(bala_train.shape)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"2611\n",
"(54831, 5)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sxNllOVhrS3V"
},
"source": [
"The model is a clear improvement over *unbalanced* BERT"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 172
},
"id": "727Qns01pOcy",
"outputId": "30d3d05e-3c3a-47e9-96a7-1ac517232d54"
},
"source": [
"pipe_b2 = make_pipeline(\n",
" HashingVectorizer(analyzer='char', n_features=300_000, ngram_range=(3, 6)), \n",
" LogisticRegression(max_iter=1000, solver='saga', class_weight='balanced')\n",
")\n",
"pipe_b2.fit(bala_train.text, bala_train.label_id)\n",
"display(get_classification_report(dev_balanced.label_id, pipe_b2.predict_proba(dev_balanced.text), labels=all_labels)[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>p</th>\n",
" <th>r</th>\n",
" <th>f</th>\n",
" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.721545</td>\n",
" <td>0.724490</td>\n",
" <td>0.723014</td>\n",
" <td>490.0</td>\n",
" <td>0.891586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.630975</td>\n",
" <td>0.673469</td>\n",
" <td>0.651530</td>\n",
" <td>490.0</td>\n",
" <td>0.827670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.740659</td>\n",
" <td>0.687755</td>\n",
" <td>0.713228</td>\n",
" <td>490.0</td>\n",
" <td>0.882229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.697726</td>\n",
" <td>0.695238</td>\n",
" <td>0.695924</td>\n",
" <td>490.0</td>\n",
" <td>0.867162</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.721545 0.724490 0.723014 490.0 0.891586\n",
"neutral 0.630975 0.673469 0.651530 490.0 0.827670\n",
"positive 0.740659 0.687755 0.713228 490.0 0.882229\n",
"mean 0.697726 0.695238 0.695924 490.0 0.867162"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nJGdWU1Rptkz"
},
"source": [
"Repeat the experiment with a larger-sized resampled train data \n",
"\n",
"The full train sample fails because of RAM requirements, so we use a fraction of mostly 0.3 of the original data.\n",
"\n",
"And still this model is only fractionally worse than BERT. "
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
},
"id": "Zg57YUikppNg",
"outputId": "447f6937-9208-4758-9c7a-0c988070439b"
},
"source": [
"group_size = int(train_data.shape[0] * 0.3 / train_data.source.nunique() / train_data.label.nunique())\n",
"print(group_size)\n",
"bala_train = train_data.groupby(['source', 'label']).apply(lambda x: x.sample(group_size, random_state=1, replace=(x.shape[0] < group_size)))\n",
"bala_train['label_id'] = [all_labels.index(xl) for xl in bala_train['label']]\n",
"print(bala_train.shape)\n",
"\n",
"pipe_b2 = make_pipeline(\n",
" HashingVectorizer(analyzer='char', n_features=300_000, ngram_range=(3, 6)), \n",
" LogisticRegression(max_iter=1000, solver='saga', class_weight='balanced')\n",
")\n",
"pipe_b2.fit(bala_train.text, bala_train.label_id)\n",
"display(get_classification_report(dev_balanced.label_id, pipe_b2.predict_proba(dev_balanced.text), labels=all_labels)[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"7834\n",
"(164514, 5)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>p</th>\n",
" <th>r</th>\n",
" <th>f</th>\n",
" <th>s</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>negative</th>\n",
" <td>0.740443</td>\n",
" <td>0.751020</td>\n",
" <td>0.745694</td>\n",
" <td>490.0</td>\n",
" <td>0.904516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>neutral</th>\n",
" <td>0.656546</td>\n",
" <td>0.706122</td>\n",
" <td>0.680433</td>\n",
" <td>490.0</td>\n",
" <td>0.849898</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive</th>\n",
" <td>0.782511</td>\n",
" <td>0.712245</td>\n",
" <td>0.745726</td>\n",
" <td>490.0</td>\n",
" <td>0.893027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.726500</td>\n",
" <td>0.723129</td>\n",
" <td>0.723951</td>\n",
" <td>490.0</td>\n",
" <td>0.882480</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" p r f s a\n",
"negative 0.740443 0.751020 0.745694 490.0 0.904516\n",
"neutral 0.656546 0.706122 0.680433 490.0 0.849898\n",
"positive 0.782511 0.712245 0.745726 490.0 0.893027\n",
"mean 0.726500 0.723129 0.723951 490.0 0.882480"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6-jSKtABxmkZ",
"outputId": "60566169-a858-4a37-8c4a-cce0b7a9a062"
},
"source": [
"import joblib\n",
"joblib.dump(pipe_b2, '/gd/MyDrive/models/ru_sentiment_balanced_logreg_classifier.joblib')"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['/gd/MyDrive/models/ru_sentiment_balanced_logreg_classifier.joblib']"
]
},
"metadata": {},
"execution_count": 25
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "evJjOfJZvwP4"
},
"source": [
"### Evaluate across sources"
]
},
{
"cell_type": "code",
"metadata": {
"id": "43yBh99gv13Z"
},
"source": [
"dev_dataloader_fixed = DataLoader(\n",
" data_tokenized['dev'], \n",
" batch_size=32, drop_last=False, shuffle=False, num_workers=0, collate_fn=data_collator\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7wTKqnKyv7KU"
},
"source": [
"dev = full_sentiment_data[full_sentiment_data.split=='dev'].copy()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"09581ac62f474fc0901565d6279b14eb",
"b10da7cb8e3642ec91ac22d051914c57",
"ab7d7e7ce2fb4368a59706cf3a7ed8b4",
"740ff11609d7450e830702285c4cf841",
"ac20354d02b2491695db0479408895ef",
"e607cb4d27c6401fa3f9e7607f8c1fc8",
"5c93a5d157fe41178277ef095c6948b1",
"8a190565b3a540a3b5de4efc66b7a6d7",
"3baf8826eef94c03b8affea159a48856",
"7af3bfa6c653425b87145c6d8c04404c",
"0fd7c6ccbcdd430db90f9e20e8ebbc3e"
]
},
"id": "1wjX2Q4dwCbN",
"outputId": "16498542-5850-4c9c-f106-2c3c2d3d4c1a"
},
"source": [
"facts, preds_m2 = predict_with_model(model2, dev_dataloader_fixed)\n",
"assert (dev.label == [all_labels[i] for i in facts]).all()\n",
"dev['preds_m2'] = [all_labels[i] for i in preds_m2.argmax(axis=1)]"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "09581ac62f474fc0901565d6279b14eb",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/2147 [00:00<?, ?it/s]"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"c36c33be83ab45dfb93ac0006bb2c1c7",
"049d077feb0a4c23b2dc278c323e4ef9",
"be3de1652596456ab2467b2989627e8b",
"5f98048a380f478d9fea46fd90ace5bf",
"ddcc7b0dedee480393da0fe4b61e4121",
"8c65eb77e5204e76a4d4e60c96bb1c83",
"b6e2e8ffe97e428dbea8d6979de46476",
"e0eab91b11724c11835e8d9ceb33ccb4",
"38a14f379d0c4e90845c8bd90c826498",
"1f377f0f9c874ac89721a4b1f09d6e6f",
"1f86fca58a2d488ab869b09d3b92bde6"
]
},
"id": "zka0T3BLzQ-l",
"outputId": "f5fe0967-70f2-498d-898f-8c8b5520a67e"
},
"source": [
"facts, preds_m1 = predict_with_model(model1, dev_dataloader_fixed)\n",
"dev['preds_m1'] = [all_labels[i] for i in preds_m1.argmax(axis=1)]"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c36c33be83ab45dfb93ac0006bb2c1c7",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/2147 [00:00<?, ?it/s]"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3B1tNtpQzoKu"
},
"source": [
"dev['preds_lr'] = [all_labels[i] for i in pipe_b2.predict(dev['text'])]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 367
},
"id": "7AahjsBnzzB9",
"outputId": "18f7ec08-ff77-44d1-869c-4156f7f9d75a"
},
"source": [
"stat_m1 = dev.groupby('source').apply(lambda x: pd.Series({\n",
" 'n': x.shape[0],\n",
" 'accuracy': accuracy_score(x.label, x.preds_m1), \n",
" 'f1_macro': f1_score(x.label, x.preds_m1, average='macro')\n",
"}))\n",
"display(stat_m1)\n",
"print(stat_m1.mean())"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n</th>\n",
" <th>accuracy</th>\n",
" <th>f1_macro</th>\n",
" </tr>\n",
" <tr>\n",
" <th>source</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>SentiRuEval2016_banks</th>\n",
" <td>1055.0</td>\n",
" <td>0.869194</td>\n",
" <td>0.709848</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SentiRuEval2016_tele</th>\n",
" <td>937.0</td>\n",
" <td>0.707577</td>\n",
" <td>0.634077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>kaggle_news</th>\n",
" <td>840.0</td>\n",
" <td>0.651190</td>\n",
" <td>0.604491</td>\n",
" </tr>\n",
" <tr>\n",
" <th>linis</th>\n",
" <td>2675.0</td>\n",
" <td>0.601495</td>\n",
" <td>0.525590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mokoron</th>\n",
" <td>51886.0</td>\n",
" <td>0.996203</td>\n",
" <td>0.996019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rureviews</th>\n",
" <td>9171.0</td>\n",
" <td>0.737651</td>\n",
" <td>0.740745</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rusentiment</th>\n",
" <td>2112.0</td>\n",
" <td>0.643466</td>\n",
" <td>0.549202</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n accuracy f1_macro\n",
"source \n",
"SentiRuEval2016_banks 1055.0 0.869194 0.709848\n",
"SentiRuEval2016_tele 937.0 0.707577 0.634077\n",
"kaggle_news 840.0 0.651190 0.604491\n",
"linis 2675.0 0.601495 0.525590\n",
"mokoron 51886.0 0.996203 0.996019\n",
"rureviews 9171.0 0.737651 0.740745\n",
"rusentiment 2112.0 0.643466 0.549202"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"n 9810.857143\n",
"accuracy 0.743825\n",
"f1_macro 0.679996\n",
"dtype: float64\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 367
},
"id": "VUzNyEuhzE2k",
"outputId": "2b019098-9aaa-4f20-99ff-62d5bcb9d85d"
},
"source": [
"stat_m2 = dev.groupby('source').apply(lambda x: pd.Series({\n",
" 'n': x.shape[0],\n",
" 'accuracy': accuracy_score(x.label, x.preds_m2), \n",
" 'f1_macro': f1_score(x.label, x.preds_m2, average='macro')\n",
"}))\n",
"display(stat_m2)\n",
"print(stat_m2.mean())"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n</th>\n",
" <th>accuracy</th>\n",
" <th>f1_macro</th>\n",
" </tr>\n",
" <tr>\n",
" <th>source</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>SentiRuEval2016_banks</th>\n",
" <td>1055.0</td>\n",
" <td>0.895735</td>\n",
" <td>0.821613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SentiRuEval2016_tele</th>\n",
" <td>937.0</td>\n",
" <td>0.779082</td>\n",
" <td>0.738880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>kaggle_news</th>\n",
" <td>840.0</td>\n",
" <td>0.641667</td>\n",
" <td>0.644934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>linis</th>\n",
" <td>2675.0</td>\n",
" <td>0.558131</td>\n",
" <td>0.508081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mokoron</th>\n",
" <td>51886.0</td>\n",
" <td>0.942778</td>\n",
" <td>0.938706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rureviews</th>\n",
" <td>9171.0</td>\n",
" <td>0.724894</td>\n",
" <td>0.726662</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rusentiment</th>\n",
" <td>2112.0</td>\n",
" <td>0.663352</td>\n",
" <td>0.648349</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n accuracy f1_macro\n",
"source \n",
"SentiRuEval2016_banks 1055.0 0.895735 0.821613\n",
"SentiRuEval2016_tele 937.0 0.779082 0.738880\n",
"kaggle_news 840.0 0.641667 0.644934\n",
"linis 2675.0 0.558131 0.508081\n",
"mokoron 51886.0 0.942778 0.938706\n",
"rureviews 9171.0 0.724894 0.726662\n",
"rusentiment 2112.0 0.663352 0.648349"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"n 9810.857143\n",
"accuracy 0.743663\n",
"f1_macro 0.718175\n",
"dtype: float64\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 367
},
"id": "ujIxhvFuz02n",
"outputId": "30329e34-cb68-40ed-a3ae-7aa14a07179c"
},
"source": [
"stat_lr = dev.groupby('source').apply(lambda x: pd.Series({\n",
" 'n': x.shape[0],\n",
" 'accuracy': accuracy_score(x.label, x.preds_lr), \n",
" 'f1_macro': f1_score(x.label, x.preds_lr, average='macro')\n",
"}))\n",
"display(stat_lr)\n",
"print(stat_lr.mean())"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n</th>\n",
" <th>accuracy</th>\n",
" <th>f1_macro</th>\n",
" </tr>\n",
" <tr>\n",
" <th>source</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>SentiRuEval2016_banks</th>\n",
" <td>1055.0</td>\n",
" <td>0.881517</td>\n",
" <td>0.803410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SentiRuEval2016_tele</th>\n",
" <td>937.0</td>\n",
" <td>0.772679</td>\n",
" <td>0.734903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>kaggle_news</th>\n",
" <td>840.0</td>\n",
" <td>0.685714</td>\n",
" <td>0.678902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>linis</th>\n",
" <td>2675.0</td>\n",
" <td>0.570093</td>\n",
" <td>0.521029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mokoron</th>\n",
" <td>51886.0</td>\n",
" <td>0.804745</td>\n",
" <td>0.767611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rureviews</th>\n",
" <td>9171.0</td>\n",
" <td>0.720532</td>\n",
" <td>0.722225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rusentiment</th>\n",
" <td>2112.0</td>\n",
" <td>0.675189</td>\n",
" <td>0.658313</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n accuracy f1_macro\n",
"source \n",
"SentiRuEval2016_banks 1055.0 0.881517 0.803410\n",
"SentiRuEval2016_tele 937.0 0.772679 0.734903\n",
"kaggle_news 840.0 0.685714 0.678902\n",
"linis 2675.0 0.570093 0.521029\n",
"mokoron 51886.0 0.804745 0.767611\n",
"rureviews 9171.0 0.720532 0.722225\n",
"rusentiment 2112.0 0.675189 0.658313"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"n 9810.857143\n",
"accuracy 0.730067\n",
"f1_macro 0.698056\n",
"dtype: float64\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Oec5HYyQ0aw3"
},
"source": [
"И всё та же статистика - на сбалансированной выборке, чтобы понять, как дисбаланс влияет на все эти числа. \n",
"\n",
"Все числа стали больше, но видим, что модель очень хорошо отрабатывает на Мокороне, и очень плохо - на почти всех остальных данных. Увы. Придётся работать с чем есть. \n",
"\n",
"В целом, второй берт получился относительно ничего. Надеюсь, на своих данных его можно будет нормально дотюнивать. "
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"c4035088cfea4cf59a15209fcec83e93",
"15eb959a6ec5492da26c6d2fccb7d476",
"5a9f1390500d4322b271761968e5f79d",
"11e1f485821248adb7dc3ad49b991fc3",
"100797ed07b4443bbb90b57f839db0b2",
"a4034f9111d6402d97563c9756d85176",
"2e15788e75f84684a1d9a01ce9be8305",
"77d735ab71f8490cbf3475bc58e91deb",
"69fe1209fec742a2b185c29e4b9760ee",
"470d65a0957a455990b8da23e51e9be8",
"2738beaba1d84e758f6ac4bdda14ee31",
"70f00d23bb3b4d80a45f90af5e62666e",
"b9a2e6e48a674361a060b55127718006",
"7b765d403fe7448b85a60386500aae87",
"5f7c34ff3ace4d218ec1764f3aad2f18",
"1e9a688103b2429a887bf311ccf51936",
"77e78015594a4cc8904321400f8d56b5",
"29586ceb7da24e86b175ceec848277b0",
"aa65cf150cf44d15a705120c68a6b7e6",
"a4f14fc3f355468d953e64b23bfce7ea",
"8fc0cccce6eb4ce2858fc0c9f4b412c2",
"38bb38b3760e4badbaf4f5e4dd3e0def"
]
},
"id": "TC8a9ohM06Jg",
"outputId": "ae67ee77-1426-4308-9529-a099b5804faf"
},
"source": [
"facts, preds_m2 = predict_with_model(model2, torch_dev_balanced_loader)\n",
"assert (dev_balanced.label == [all_labels[i] for i in facts]).all()\n",
"dev_balanced['preds_m2'] = [all_labels[i] for i in preds_m2.argmax(axis=1)]\n",
"\n",
"facts, preds_m1 = predict_with_model(model1, torch_dev_balanced_loader)\n",
"dev_balanced['preds_m1'] = [all_labels[i] for i in preds_m1.argmax(axis=1)]\n",
"\n",
"dev_balanced['preds_lr'] = [all_labels[i] for i in pipe_b2.predict(dev_balanced['text'])]"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c4035088cfea4cf59a15209fcec83e93",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "70f00d23bb3b4d80a45f90af5e62666e",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/23 [00:00<?, ?it/s]"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 367
},
"id": "156CKIZj1LxJ",
"outputId": "5b160945-e5bf-424e-fbb5-ade7ec041809"
},
"source": [
"stat_m1 = dev_balanced.groupby('source').apply(lambda x: pd.Series({\n",
" 'n': x.shape[0],\n",
" 'accuracy': accuracy_score(x.label, x.preds_m1), \n",
" 'f1_macro': f1_score(x.label, x.preds_m1, average='macro')\n",
"}))\n",
"display(stat_m1)\n",
"print(stat_m1.mean())"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n</th>\n",
" <th>accuracy</th>\n",
" <th>f1_macro</th>\n",
" </tr>\n",
" <tr>\n",
" <th>source</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>SentiRuEval2016_banks</th>\n",
" <td>210.0</td>\n",
" <td>0.676190</td>\n",
" <td>0.643551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SentiRuEval2016_tele</th>\n",
" <td>210.0</td>\n",
" <td>0.585714</td>\n",
" <td>0.574823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>kaggle_news</th>\n",
" <td>210.0</td>\n",
" <td>0.552381</td>\n",
" <td>0.539622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>linis</th>\n",
" <td>210.0</td>\n",
" <td>0.480952</td>\n",
" <td>0.460348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mokoron</th>\n",
" <td>210.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rureviews</th>\n",
" <td>210.0</td>\n",
" <td>0.738095</td>\n",
" <td>0.740806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rusentiment</th>\n",
" <td>210.0</td>\n",
" <td>0.566667</td>\n",
" <td>0.538639</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n accuracy f1_macro\n",
"source \n",
"SentiRuEval2016_banks 210.0 0.676190 0.643551\n",
"SentiRuEval2016_tele 210.0 0.585714 0.574823\n",
"kaggle_news 210.0 0.552381 0.539622\n",
"linis 210.0 0.480952 0.460348\n",
"mokoron 210.0 1.000000 1.000000\n",
"rureviews 210.0 0.738095 0.740806\n",
"rusentiment 210.0 0.566667 0.538639"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"n 210.000000\n",
"accuracy 0.657143\n",
"f1_macro 0.642541\n",
"dtype: float64\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 367
},
"id": "vJSqkTNI1LxK",
"outputId": "f47cb74f-725d-458b-c6a5-f999164c3098"
},
"source": [
"stat_m2 = dev_balanced.groupby('source').apply(lambda x: pd.Series({\n",
" 'n': x.shape[0],\n",
" 'accuracy': accuracy_score(x.label, x.preds_m2), \n",
" 'f1_macro': f1_score(x.label, x.preds_m2, average='macro')\n",
"}))\n",
"display(stat_m2)\n",
"print(stat_m2.mean())"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n</th>\n",
" <th>accuracy</th>\n",
" <th>f1_macro</th>\n",
" </tr>\n",
" <tr>\n",
" <th>source</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>SentiRuEval2016_banks</th>\n",
" <td>210.0</td>\n",
" <td>0.833333</td>\n",
" <td>0.830800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SentiRuEval2016_tele</th>\n",
" <td>210.0</td>\n",
" <td>0.742857</td>\n",
" <td>0.741517</td>\n",
" </tr>\n",
" <tr>\n",
" <th>kaggle_news</th>\n",
" <td>210.0</td>\n",
" <td>0.657143</td>\n",
" <td>0.659544</td>\n",
" </tr>\n",
" <tr>\n",
" <th>linis</th>\n",
" <td>210.0</td>\n",
" <td>0.500000</td>\n",
" <td>0.495627</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mokoron</th>\n",
" <td>210.0</td>\n",
" <td>0.980952</td>\n",
" <td>0.980839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rureviews</th>\n",
" <td>210.0</td>\n",
" <td>0.719048</td>\n",
" <td>0.719883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rusentiment</th>\n",
" <td>210.0</td>\n",
" <td>0.666667</td>\n",
" <td>0.668986</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n accuracy f1_macro\n",
"source \n",
"SentiRuEval2016_banks 210.0 0.833333 0.830800\n",
"SentiRuEval2016_tele 210.0 0.742857 0.741517\n",
"kaggle_news 210.0 0.657143 0.659544\n",
"linis 210.0 0.500000 0.495627\n",
"mokoron 210.0 0.980952 0.980839\n",
"rureviews 210.0 0.719048 0.719883\n",
"rusentiment 210.0 0.666667 0.668986"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"n 210.000000\n",
"accuracy 0.728571\n",
"f1_macro 0.728171\n",
"dtype: float64\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 367
},
"id": "CwOnYYp_1LxK",
"outputId": "1a8b3332-8c77-4263-9ee6-a9f5be92375c"
},
"source": [
"stat_lr = dev_balanced.groupby('source').apply(lambda x: pd.Series({\n",
" 'n': x.shape[0],\n",
" 'accuracy': accuracy_score(x.label, x.preds_lr), \n",
" 'f1_macro': f1_score(x.label, x.preds_lr, average='macro')\n",
"}))\n",
"display(stat_lr)\n",
"print(stat_lr.mean())"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n</th>\n",
" <th>accuracy</th>\n",
" <th>f1_macro</th>\n",
" </tr>\n",
" <tr>\n",
" <th>source</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>SentiRuEval2016_banks</th>\n",
" <td>210.0</td>\n",
" <td>0.838095</td>\n",
" <td>0.837759</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SentiRuEval2016_tele</th>\n",
" <td>210.0</td>\n",
" <td>0.780952</td>\n",
" <td>0.779683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>kaggle_news</th>\n",
" <td>210.0</td>\n",
" <td>0.719048</td>\n",
" <td>0.722450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>linis</th>\n",
" <td>210.0</td>\n",
" <td>0.547619</td>\n",
" <td>0.547372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mokoron</th>\n",
" <td>210.0</td>\n",
" <td>0.800000</td>\n",
" <td>0.798673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rureviews</th>\n",
" <td>210.0</td>\n",
" <td>0.695238</td>\n",
" <td>0.695740</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rusentiment</th>\n",
" <td>210.0</td>\n",
" <td>0.680952</td>\n",
" <td>0.681018</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n accuracy f1_macro\n",
"source \n",
"SentiRuEval2016_banks 210.0 0.838095 0.837759\n",
"SentiRuEval2016_tele 210.0 0.780952 0.779683\n",
"kaggle_news 210.0 0.719048 0.722450\n",
"linis 210.0 0.547619 0.547372\n",
"mokoron 210.0 0.800000 0.798673\n",
"rureviews 210.0 0.695238 0.695740\n",
"rusentiment 210.0 0.680952 0.681018"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"n 210.000000\n",
"accuracy 0.723129\n",
"f1_macro 0.723242\n",
"dtype: float64\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fW-Igrh-owys"
},
"source": [
"# Usage"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UqKJkMjfoyq5",
"outputId": "3752f25b-1040-4c73-e461-63cb25cb4516"
},
"source": [
"!pip install transformers sentencepiece --quiet"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[K |████████████████████████████████| 2.6 MB 8.9 MB/s \n",
"\u001b[K |████████████████████████████████| 1.2 MB 85.1 MB/s \n",
"\u001b[K |████████████████████████████████| 636 kB 53.9 MB/s \n",
"\u001b[K |████████████████████████████████| 895 kB 66.2 MB/s \n",
"\u001b[K |████████████████████████████████| 3.3 MB 55.1 MB/s \n",
"\u001b[?25h"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "N3oY-PVeox9q"
},
"source": [
"import torch\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
"\n",
"model_checkpoint = 'cointegrated/rubert-tiny-sentiment-balanced'\n",
"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
"model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
"if torch.cuda.is_available():\n",
" model.cuda()"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YJbbikRwpMrt",
"outputId": "cc4658e3-db99-44fd-82d9-d8e31c207362"
},
"source": [
"def get_sentiment(text, return_type='label'):\n",
" \"\"\" Calculate sentiment of a text. `return_type` can be 'label', 'score' or 'proba' \"\"\"\n",
" with torch.no_grad():\n",
" inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(model.device)\n",
" proba = torch.sigmoid(model(**inputs).logits).cpu().numpy()[0]\n",
" if return_type == 'label':\n",
" return model.config.id2label[proba.argmax()]\n",
" elif return_type == 'score':\n",
" return proba.dot([-1, 0, 1])\n",
" return proba\n",
"\n",
"text = 'Какая гадость эта ваша заливная рыба!'\n",
"# classify the text\n",
"print(get_sentiment(text, 'label')) # negative\n",
"# score the text on the scale from -1 (very negative) to +1 (very positive)\n",
"print(get_sentiment(text, 'score')) # -0.5894946306943893\n",
"# calculate probabilities of all labels\n",
"print(get_sentiment(text, 'proba')) # [0.7870447 0.4947824 0.19755007]"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"negative\n",
"-0.5894946306943893\n",
"[0.7870447 0.4947824 0.19755007]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pbjJ29bppZgx"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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