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| "model_module": "@jupyter-widgets/base", | |
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| } | |
| } | |
| } | |
| }, | |
| "accelerator": "GPU" | |
| }, | |
| "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" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "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", | |
| "e34236c74c104521a223262d5890bef5", | |
| "c9c2ce6d5a3e4974857f79e35a2b0d00", | |
| "b53dc0c925f84919a551c40c562c3b7a", | |
| "f286aabd57ec47f899482e6247650549", | |
| "cf4ca6ddd9f045bc9c844f40cdeb76ad", | |
| "36446a8099b9457f884b7a78ae0c67f7", | |
| "b4cb1bbeacae4a9ebb19526ed2897e65", | |
| "4c20f41be90b4351a7c5f8cea6d8d3bc", | |
| "d08ab018e56240099b717669f32e767c", | |
| "b75b6b7f4c0c4831ad92d6b21666a63c", | |
| "6145ec93464b4a44adbc5531d58bd750", | |
| "da957c162b3944258977584715209005", | |
| "134d27ffcaa549be997e89eb62cc9ec6", | |
| "ec354dae0bfc41f08112b62819b86071", | |
| "514d96e9a24f485ba0f0419fa489cc94", | |
| "c3724ccfc3564d52b42d6d8547263fe9", | |
| "fbf7f9a4f0964df69bbafa653bf4aa1a", | |
| "62c8dfc63b1a4c3d8096b4701fea4140", | |
| "0176789d72ef41839234af84e0685eea", | |
| "b8aec90289f54a0e835c63bf64c3d61f", | |
| "688cbeb12294402896abf8f415428a81", | |
| "d5d43924f37d4303a5587b2237353462", | |
| "1596d4b0d85240618d50f9f0a61ba491", | |
| "3eefa018bdf944c7b5a508594ff5093b", | |
| "b1ff9ed1b7e64339979971f0fe2b88ab", | |
| "07776e5d08544fcda16c90be8828bc2f", | |
| "52e11b3260384d21be95ed0e647fcbb6", | |
| "b4b56bf76afe412da7c9c3baab3805e4", | |
| "bc2fd00e79b5409eaf944e0306b98830", | |
| "2d1fda30ce20406784d47f2552f208c9", | |
| "c9a62d1453a8417c85889dddbb62bec8", | |
| "364909cef92d4a5d978ba9903ef55bd3", | |
| "7da13921a44c4de8aaa125d7b630f46d", | |
| "4b20db932791445dafae864d7fbe06dc", | |
| "cd0a736413c342eaba8064560de6edeb", | |
| "1cc7bf80edc54662867d0563bea137a5", | |
| "b0acac64bd9d4712a5c488dd6a8600cf", | |
| "571567493d564f2384cdd62f486f20ea", | |
| "a8b1ac0e5464422da10f8622e234c17f", | |
| "45665ba4ae07492f88d2c11e6237e366", | |
| "2c7c812939004bc6a83d4fab37888fa8", | |
| "c403e06770c14242b756c9ab9c03b6d7", | |
| "1212df45e2cd41c5a387fdaa092a60c9" | |
| ] | |
| }, | |
| "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]" | |
| ] | |
| }, | |
| "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", | |
| "5933dcc3b49d4163be3a1026177659b7", | |
| "92139d9d223d4bda8bd9e539d990aa95", | |
| "8cd45dc3207c4593a98c26e4f6a13410", | |
| "9c44a463d3354efcbc9969a3bf9b0448", | |
| "7c8e0b0b9a69428394d723ed3d2229af", | |
| "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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| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
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| " <th></th>\n", | |
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| " <th>0</th>\n", | |
| " <td>0.267435</td>\n", | |
| " <td>0.421399</td>\n", | |
| " <td>0.327210</td>\n", | |
| " <td>16253.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>0.670845</td>\n", | |
| " <td>0.539410</td>\n", | |
| " <td>0.597991</td>\n", | |
| " <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", | |
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| " <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", | |
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| " 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" | |
| ] | |
| }, | |
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| { | |
| "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, | |
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| "06536b50d5324b67ab088586fc48ce87", | |
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| }, | |
| "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": { | |
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| "version_major": 2 | |
| }, | |
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| " 0%| | 0/3 [00:00<?, ?it/s]" | |
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| "metadata": {} | |
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| { | |
| "output_type": "display_data", | |
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| }, | |
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| " 0%| | 0/8570 [00:00<?, ?it/s]" | |
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| "metadata": {} | |
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| " 0%| | 0/8570 [00:00<?, ?it/s]" | |
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| { | |
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| " 0%| | 0/8570 [00:00<?, ?it/s]" | |
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| { | |
| "output_type": "display_data", | |
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| }, | |
| "text/plain": [ | |
| " 0%| | 0/1074 [00:00<?, ?it/s]" | |
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| }, | |
| "metadata": {} | |
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| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
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| " <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", | |
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| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
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| "source": [ | |
| "" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "3c3bbd9822134f2e915e56b421575ede", | |
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| " 0%| | 0/2 [00:00<?, ?ba/s]" | |
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| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| { | |
| "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", | |
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| "5162dc6c6848496d8aac1217e482f63b", | |
| "3bcd21b7f25341febee83bfc4e4d6fd6", | |
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| "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": [] | |
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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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| "text/plain": [ | |
| " 0%| | 0/25707 [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
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| " </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" | |
| }, | |
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| " </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", | |
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| " <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", | |
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| "</div>" | |
| ], | |
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| "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" | |
| ] | |
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| }, | |
| { | |
| "output_type": "stream", | |
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| "aucs: [0.908183048729696, 0.8388765097875885, 0.9038067471886714] 0.8836221019019853\n", | |
| "step 6000: train loss: 0.5629 val auc: 0.8836221019019853\n" | |
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| " <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", | |
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| " <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", | |
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| "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" | |
| ] | |
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| { | |
| "output_type": "stream", | |
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| "aucs: [0.9105508121615993, 0.845600791336943, 0.9045116618075802] 0.8868877551020408\n", | |
| "step 9000: train loss: 0.5022 val auc: 0.8868877551020408\n" | |
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| " <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", | |
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| " <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", | |
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| " <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", | |
| " <td>0.889806</td>\n", | |
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| "negative 0.731939 0.785714 0.757874 490.0 0.913091\n", | |
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| "positive 0.773784 0.746939 0.760125 490.0 0.903426\n", | |
| "mean 0.729792 0.729932 0.729378 490.0 0.889806" | |
| ] | |
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| { | |
| "output_type": "stream", | |
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| "aucs: [0.9130914202415661, 0.8529019158683881, 0.9034256559766765] 0.8898063306955436\n", | |
| "step 12000: train loss: 0.4585 val auc: 0.8898063306955436\n" | |
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| " <td>490.0</td>\n", | |
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| " <td>0.681529</td>\n", | |
| " <td>0.655102</td>\n", | |
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| " <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", | |
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| " <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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| "mean 0.726192 0.726531 0.725904 490.0 0.889651" | |
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| "aucs: [0.9140410245730947, 0.8547573927530195, 0.9001541024573095] 0.8896508399278079\n", | |
| "step 15000: train loss: 0.4233 val auc: 0.8896508399278079\n" | |
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| " <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", | |
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| " <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>" | |
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| "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": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
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| "aucs: [0.909411703456893, 0.8515191586838817, 0.8994752186588921] 0.8868020269332223\n", | |
| "step 18000: train loss: 0.3871 val auc: 0.8868020269332223\n" | |
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| " <td>0.767347</td>\n", | |
| " <td>0.763452</td>\n", | |
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| " <td>490.0</td>\n", | |
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| " <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", | |
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| "positive 0.774194 0.734694 0.753927 490.0 0.891818\n", | |
| "mean 0.732178 0.730612 0.731126 490.0 0.886243" | |
| ] | |
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| "metadata": {} | |
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| "aucs: [0.9124645980841316, 0.8544471053727613, 0.8918179925031237] 0.8862432319866721\n", | |
| "step 21000: train loss: 0.3658 val auc: 0.8862432319866721\n" | |
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| " <td>0.778993</td>\n", | |
| " <td>0.726531</td>\n", | |
| " <td>0.751848</td>\n", | |
| " <td>490.0</td>\n", | |
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| " <td>0.665306</td>\n", | |
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| " <td>490.0</td>\n", | |
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| " <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", | |
| " <td>0.885796</td>\n", | |
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| "positive 0.736328 0.769388 0.752495 490.0 0.894698\n", | |
| "mean 0.722007 0.720408 0.720755 490.0 0.885796" | |
| ] | |
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| { | |
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| "aucs: [0.9113546438983756, 0.851335901707622, 0.8946980424822991] 0.8857961960294323\n", | |
| "step 24000: train loss: 0.3407 val auc: 0.8857961960294323\n" | |
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| " </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", | |
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| " <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", | |
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| " <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", | |
| " </tr>\n", | |
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| " p r f s a\n", | |
| "negative 0.750484 0.791837 0.770606 490.0 0.912315\n", | |
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| "positive 0.793182 0.712245 0.750538 490.0 0.891624\n", | |
| "mean 0.737427 0.734694 0.735031 490.0 0.886125" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
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| "text": [ | |
| "aucs: [0.9123146605581006, 0.8544366930445648, 0.8916243231986672] 0.8861252256004443\n", | |
| "step 25706: train loss: 0.3305 val auc: 0.8861252256004443\n" | |
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| "name": "stdout" | |
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| { | |
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| "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": [ | |
| "ca5a8bc6aeea4a7cb0d926bcf707fcbc", | |
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| "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": [ | |
| { | |
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| "application/vnd.jupyter.widget-view+json": { | |
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| { | |
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| "base_uri": "https://localhost:8080/", | |
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| "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", | |
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| " 0%| | 0/23 [00:00<?, ?it/s]" | |
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| " <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", | |
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| " <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", | |
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| " 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": {} | |
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| { | |
| "output_type": "stream", | |
| "text": [ | |
| "aucs: [0.8824854227405248, 0.813036234902124, 0.8829039983340274] 0.859475218658892\n" | |
| ], | |
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| "version_minor": 0, | |
| "version_major": 2 | |
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| "text/plain": [ | |
| " 0%| | 0/23 [00:00<?, ?it/s]" | |
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| "data": { | |
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| " </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", | |
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| " 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" | |
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| { | |
| "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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