{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "r3KQZNbsfQgH" }, "outputs": [], "source": [ "# from transformers import AutoTokenizer, AutoModel\n", "import numpy as np\n", "# from sklearn.metrics.pairwise import cosine_similarity\n", "import torch\n", "import pandas as pd\n", "import re\n", "import nltk\n", "from nltk.corpus import stopwords\n", "# from nltk.stem import WordNetLemmatizer\n", "# import pymorphy2\n", "import string" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X1dtk2CzfQgJ", "outputId": "561e0d71-6f4c-4026-ea15-0bf8ea27420c" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package stopwords to /home/vera/nltk_data...\n", "[nltk_data] Package stopwords is already up-to-date!\n", "[nltk_data] Downloading package wordnet to /home/vera/nltk_data...\n", "[nltk_data] Package wordnet is already up-to-date!\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nltk.download('stopwords')\n", "nltk.download('wordnet')" ] }, { "cell_type": "markdown", "metadata": { "id": "ie_DSFN7fQgK" }, "source": [ "# Sentence Transformers" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "dUoOgeHrf3cF", "outputId": "4b479581-f3b1-432c-8bd3-83c34550f984" }, "outputs": [], "source": [ "# !pip install sentence_transformers" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_QIPFmnigUL3", "outputId": "b885e978-22f8-4e5d-e3e4-c4fd99d6c6bd" }, "outputs": [], "source": [ "# !pip install faiss-cpu" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "2MlyTJJsPViK" }, "outputs": [], "source": [ "stop_words = set(stopwords.words('russian'))\n", "# lemmatizer = WordNetLemmatizer()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "MWnXMZ91fQgL" }, "outputs": [], "source": [ "from sentence_transformers import SentenceTransformer, util\n", "import faiss" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('merged_file.csv')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "20n_cn6kfQgL" }, "outputs": [], "source": [ "data['description'] = data['description'].astype(str)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "vUjK6pXTfQgM" }, "outputs": [], "source": [ "data['annotation_len'] = data['description'].apply(lambda x: len(str(x).split()) if pd.notnull(x) else 0)\n", "data = data[data['annotation_len'] > 10] # Отсечение слишком коротких аннотаций" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "dG_VW9WVfQgM", "outputId": "cfaad9d4-eeb8-42df-8a25-8a505740e577" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 6809 entries, 0 to 6887\n", "Data columns (total 12 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 page_url 6809 non-null object \n", " 1 image_url 6809 non-null object \n", " 2 tvshow_title 6809 non-null object \n", " 3 description 6809 non-null object \n", " 4 names 5456 non-null object \n", " 5 names_and_roles 5456 non-null object \n", " 6 genre 6613 non-null object \n", " 7 dates 6809 non-null object \n", " 8 country 6808 non-null object \n", " 9 imdb 6298 non-null float64\n", " 10 kinopoisk_rating 6003 non-null float64\n", " 11 annotation_len 6809 non-null int64 \n", "dtypes: float64(2), int64(1), object(9)\n", "memory usage: 691.5+ KB\n" ] } ], "source": [ "data.info()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "collapsed": true, "id": "KAIGNGRDfQgM", "outputId": "fe400868-6548-4f1c-f1b1-cf277849d295" }, "outputs": [ { "data": { "text/html": [ "
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page_urlimage_urltvshow_titledescriptionnamesnames_and_rolesgenredatescountryimdbkinopoisk_ratingannotation_len
0https://myshows.me/view/7718/https://media.myshows.me/shows/1920/9/49/9492c...ШерлокИстория о Шерлоке Холмсе и докторе Ватсоне в Л...Benedict Cumberbatch, Martin FreemanBenedict Cumberbatch (Sherlock Holmes), Martin...Криминал, Драма, Детектив25.07.2010 — 15.01.2017Великобритания9.18.856143
1https://myshows.me/view/11945/https://media.myshows.me/shows/1920/d/d1/dd10e...Игра престоловИгра Престолов — американский фэнтези-сериал, ...Peter Dinklage, Kit Harington, Lena Headey, Em...Peter Dinklage (Tyrion Lannister), Kit Haringt...Приключения, Драма, Фэнтези17.04.2011 — 26.05.2019США9.28.98135
2https://myshows.me/view/2/https://media.myshows.me/shows/1920/2/a9/2a932...Теория большого взрываТеория большого взрыва это, уже ставший класси...Johnny Galecki, Jim Parsons, Simon Helberg, Ku...Johnny Galecki (Leonard Leakey Hofstadter), Ji...Комедия24.09.2007 — 27.12.2019США8.18.627227
3https://myshows.me/view/45/https://media.myshows.me/shows/1920/f/c3/fc37c...СверхъестественноеБольше десяти лет назад это шоу начиналось, ка...Jared Padalecki, Jensen Ackles, Misha Collins,...Jared Padalecki (Sam Winchester), Jensen Ackle...Боевик, Драма, Сверхъестественное13.09.2005 — 19.11.2020США8.48.237174
4https://myshows.me/view/304/https://media.myshows.me/shows/1920/1/23/12314...ОтбросыКомпания мелких подонков старшего школьного во...Nathan Stewart-Jarrett, Joseph Gilgun, Antonia...Nathan Stewart-Jarrett (Curtis Donovan), Josep...Комедия, Драма, Фантастика12.11.2009 — 11.12.2013Великобритания8.28.062179
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Отбросы \n", "\n", " description \\\n", "0 История о Шерлоке Холмсе и докторе Ватсоне в Л... \n", "1 Игра Престолов — американский фэнтези-сериал, ... \n", "2 Теория большого взрыва это, уже ставший класси... \n", "3 Больше десяти лет назад это шоу начиналось, ка... \n", "4 Компания мелких подонков старшего школьного во... \n", "\n", " names \\\n", "0 Benedict Cumberbatch, Martin Freeman \n", "1 Peter Dinklage, Kit Harington, Lena Headey, Em... \n", "2 Johnny Galecki, Jim Parsons, Simon Helberg, Ku... \n", "3 Jared Padalecki, Jensen Ackles, Misha Collins,... \n", "4 Nathan Stewart-Jarrett, Joseph Gilgun, Antonia... \n", "\n", " names_and_roles \\\n", "0 Benedict Cumberbatch (Sherlock Holmes), Martin... \n", "1 Peter Dinklage (Tyrion Lannister), Kit Haringt... \n", "2 Johnny Galecki (Leonard Leakey Hofstadter), Ji... \n", "3 Jared Padalecki (Sam Winchester), Jensen Ackle... \n", "4 Nathan Stewart-Jarrett (Curtis Donovan), Josep... \n", "\n", " genre dates country \\\n", "0 Криминал, Драма, Детектив 25.07.2010 — 15.01.2017 Великобритания \n", "1 Приключения, Драма, Фэнтези 17.04.2011 — 26.05.2019 США \n", "2 Комедия 24.09.2007 — 27.12.2019 США \n", "3 Боевик, Драма, Сверхъестественное 13.09.2005 — 19.11.2020 США \n", "4 Комедия, Драма, Фантастика 12.11.2009 — 11.12.2013 Великобритания \n", "\n", " imdb kinopoisk_rating annotation_len \n", "0 9.1 8.856 143 \n", "1 9.2 8.981 35 \n", "2 8.1 8.627 227 \n", "3 8.4 8.237 174 \n", "4 8.2 8.062 179 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_66816/1946767519.py:13: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " data['clean_description'] = data['description'].apply(clean_text)\n" ] } ], "source": [ "def clean_text(text):\n", " text = re.sub(r'\\r\\n', ' ', text)\n", " text = text.replace('\\xa0', ' ')\n", " clean_pattern = re.compile(r'[^a-zA-Zа-яА-ЯёЁ0-9.,!?;:\\s]')\n", " text = clean_pattern.sub('', text)\n", " url_pattern = re.compile(r'http\\S+|www\\S+|https\\S+')\n", " text = url_pattern.sub(r'', text)\n", " text = text.translate(str.maketrans('', '', string.punctuation))\n", " text = text.lower()\n", " return text\n", "\n", "data['clean_description'] = data['description'].apply(clean_text)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import TruncatedSVD\n", "from sklearn.decomposition import FastICA" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_66816/1587781095.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " data['names_and_roles'] = data['names_and_roles'].fillna('Не указаны')\n" ] } ], "source": [ "data['names_and_roles'] = data['names_and_roles'].fillna('Не указаны')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_66816/1379828616.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " data['genre'] = data['genre'].fillna('Не указан')\n" ] } ], "source": [ "data['genre'] = data['genre'].fillna('Не указан')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ft19knhffQgO", "outputId": "8a5ce806-671d-40b2-d572-e17d11b697a9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/vera/miniforge3/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", " warnings.warn(\n" ] } ], "source": [ "# Шаг 2: Векторизация описаний сериалов с помощью Sentence Transformers\n", "# Используем модель Sentence Transformers для асимметричного семантического поиска\n", "model = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2') # all-mpnet-base-v2 #sentence-transformers/distiluse-base-multilingual-cased-v1\n", "# model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v1')\n", "# model = SentenceTransformer('sentence-transformers/LaBSE')\n", "\n", "# model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')\n", "model.to('cuda');\n", "# descriptions = data['clean_description'].tolist()\n", "# # description_embeddings = model.encode(descriptions, convert_to_tensor=True).cpu().numpy()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "descriptions = data['clean_description'].tolist()\n", "description_embeddings = model.encode(descriptions, convert_to_tensor=True)\n", "# description_embeddings = description_embeddings / torch.norm(description_embeddings, dim=1, keepdim=True) # Нормализация\n", "description_embeddings = description_embeddings.cpu().numpy()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "actors = data['names_and_roles'].tolist()\n", "actors_embeddings = model.encode(actors, convert_to_tensor=True)\n", "# actors_embeddings = actors_embeddings / torch.norm(actors_embeddings, dim=1, keepdim=True) # Нормализация\n", "actors_embeddings = actors_embeddings.cpu().numpy()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "genre = data['genre'].tolist()\n", "genre_embeddings = model.encode(genre, convert_to_tensor=True)\n", "# genre_embeddings = genre_embeddings / torch.norm(genre_embeddings, dim=1, keepdim=True) # Нормализация\n", "genre_embeddings = genre_embeddings.cpu().numpy()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "from sklearn.pipeline import make_pipeline\n", "from sklearn.preprocessing import Normalizer" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "description_weight = 0.7\n", "actors_weight = 0.15\n", "genre_weight = 0.15" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "weighted_description_embeddings = description_embeddings * description_weight\n", "weighted_actors_embeddings = actors_embeddings * actors_weight\n", "weighted_genre_embeddings = genre_embeddings * genre_weight" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "combined_embeddings = np.concatenate((weighted_description_embeddings, weighted_actors_embeddings, weighted_genre_embeddings), axis=1)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# ica = make_pipeline(FastICA(n_components=100, random_state=0), Normalizer(copy=False))\n", "# description_embeddings = ica.fit_transform(description_embeddings)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Explained variance of the SVD step: 82.7%\n" ] } ], "source": [ "lsa = make_pipeline(TruncatedSVD(n_components=100), Normalizer(copy=False))\n", "combined_embeddings = lsa.fit_transform(combined_embeddings)\n", "explained_variance = lsa[0].explained_variance_ratio_.sum()\n", "print(f\"Explained variance of the SVD step: {explained_variance * 100:.1f}%\")" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "import joblib" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['lsa_model.pkl']" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joblib.dump(lsa, 'lsa_model.pkl')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "id": "g6u6AmmdfQgO" }, "outputs": [], "source": [ "# Нормализация векторов для косинусного сходства\n", "combined_embeddings = combined_embeddings / np.linalg.norm(combined_embeddings, axis=1, keepdims=True)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "id": "1dXYKpRHfQgO" }, "outputs": [], "source": [ "# Создание индекса с использованием Faiss для косинусного сходства\n", "dimension = combined_embeddings.shape[1]\n", "index = faiss.IndexFlatIP(dimension)\n", "index.add(combined_embeddings)\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "# Обработка пользовательского запроса\n", "def search_series(user_query, top_k=10):\n", " user_query = clean_text(user_query)\n", " query_embedding = model.encode([user_query], convert_to_tensor=True).cpu().numpy()\n", " weighted_query_embedding = np.concatenate((query_embedding * description_weight,\n", " query_embedding * actors_weight,\n", " query_embedding * genre_weight), axis=1)\n", " weighted_query_embedding = lsa.transform(weighted_query_embedding)\n", " weighted_query_embedding = weighted_query_embedding / np.linalg.norm(weighted_query_embedding, axis=1, keepdims=True) # Нормализация\n", " D, I = index.search(weighted_query_embedding, top_k)\n", " results = data.iloc[I[0]].copy()\n", " cosine_similarities = D[0]\n", " results['cosine_similarity'] = cosine_similarities\n", " return results" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "kB7bArppfQgO", "outputId": "eeb46e94-a796-4a1e-8ec8-98a1035fd614" }, "outputs": [ { "data": { "text/html": [ "
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page_urlimage_urltvshow_titledescriptionnamesnames_and_rolesgenredatescountryimdbkinopoisk_ratingannotation_lenclean_descriptioncosine_similarity
19https://myshows.me/view/55/https://media.myshows.me/shows/1920/3/21/32187...КлиникаОтучившись четыре года в медицинской школе, Дж...Donald Faison, John C. McGinley, Ken Jenkins, ...Donald Faison (Dr. Christopher Duncan Turk), J...Комедия, Драма, Медицинский02.10.2001 — 17.03.2010США8.48.73146отучившись четыре года в медицинской школе джо...0.754829
5804https://myshows.me/view/64334/https://media.myshows.me/shows/1920/1/d9/1d949...МедперсоналСюжет сериала разворачивается в Торонто, где у...Nicola Correia-Damude, Cathy White, Alexandra ...Nicola Correia-Damude (Dr. Vanessa Banks), Cat...Драма, Медицинский06.01.2020 — 29.08.2021Канада6.1NaN61сюжет сериала разворачивается в торонто где уч...0.720963
3271https://myshows.me/view/65613/https://media.myshows.me/shows/1920/d/4d/d4da7...Врачебная мудростьИстории врачей и медсестёр, которые окончили о...Jo Jung Suk, Kim Dae Myung, Jung Kyung-Ho, Jun...Jo Jung Suk (Ik Joon), Kim Dae Myung (Suk Hyun...Драма, Медицинский12.03.2020 — 16.09.2021Южная Корея8.88.78015истории врачей и медсестёр которые окончили од...0.700698
4097https://myshows.me/view/20585/https://media.myshows.me/shows/1920/a/68/a68d5...КомаМолодая студентка-медик обнаруживает, что что-...Lauren Ambrose, Natalie Knepp, Michael Weston,...Lauren Ambrose (Susan Wheeler), Natalie Knepp ...Детектив, Драма, Фантастика03.09.2012 — 04.09.2012США5.86.27724молодая студенткамедик обнаруживает что чтото ...0.699154
1837https://myshows.me/view/27947/https://media.myshows.me/shows/1920/2/c0/2c0b5...Красные браслеты (Испания)По воле злого рока несколько подростков попада...Marc Balaguer, Igor Szpakowski, Joana Vilapuig...Marc Balaguer (Toni), Igor Szpakowski (Jordi),...Драма24.01.2011 — 22.04.2013Испания8.18.28750по воле злого рока несколько подростков попада...0.688771
3476https://myshows.me/view/25185/https://media.myshows.me/shows/1920/2/fb/2fb9f...Доктор Эмили ОуэнсНаконец-то Эмили Оуэнс чувствует, что повзросл...Mamie Gummer, Aja Naomi King, Justin Hartley, ...Mamie Gummer (Emily Owens), Aja Naomi King (Ca...Комедия, Драма, Романтика16.10.2012 — 05.02.2013США7.57.058136наконецто эмили оуэнс чувствует что повзрослел...0.673646
714https://myshows.me/view/53657/https://media.myshows.me/shows/1920/8/88/88898...ОрдинаторМолодой врач-ординатор Девон Правеш начинает к...Matt Czuchry, Manish Dayal, Bruce Greenwood, M...Matt Czuchry (Dr. Conrad Hawkins), Manish Daya...Драма, Медицинский, Триллер21.01.2018 — 17.01.2023США7.87.610108молодой врачординатор девон правеш начинает ка...0.656689
4074https://myshows.me/view/73498/https://media.myshows.me/shows/1920/5/8b/58b02...ПодслушаноПятеро друзей — Юра, Джулс, Лика, Крест и Кост...Александр Новиков, Валентина Ляпина, Никита Па...Александр Новиков (Юра), Валентина Ляпина (Джу...Драма, Триллер31.05.2021 — 14.09.2022Россия5.96.35665пятеро друзей юра джулс лика крест и костя з...0.639900
5154https://myshows.me/view/7676/https://media.myshows.me/shows/1920/9/27/92798...Дэцкая больницаБольница — не место для лентяев. Больница — эт...Rob Huebel, Rob Corddry, Ken Marino, Erinn Hay...Rob Huebel (Dr. Owen Maestro / Rob Huebel), Ro...Комедия, Медицинский11.07.2010 — 15.04.2016США7.86.06642больница не место для лентяев больница это м...0.639729
3106https://myshows.me/view/87793/https://media.myshows.me/shows/1920/6/4f/64f65...Доктор КризисДва гениальных доктора, которые в школе соперн...NaNНе указаныКомедия, Медицинский, Романтика27.01.2024 — 17.03.2024Южная Корея7.57.89023два гениальных доктора которые в школе соперни...0.633453
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Больница — эт... \n", "3106 Два гениальных доктора, которые в школе соперн... \n", "\n", " names \\\n", "19 Donald Faison, John C. McGinley, Ken Jenkins, ... \n", "5804 Nicola Correia-Damude, Cathy White, Alexandra ... \n", "3271 Jo Jung Suk, Kim Dae Myung, Jung Kyung-Ho, Jun... \n", "4097 Lauren Ambrose, Natalie Knepp, Michael Weston,... \n", "1837 Marc Balaguer, Igor Szpakowski, Joana Vilapuig... \n", "3476 Mamie Gummer, Aja Naomi King, Justin Hartley, ... \n", "714 Matt Czuchry, Manish Dayal, Bruce Greenwood, M... \n", "4074 Александр Новиков, Валентина Ляпина, Никита Па... \n", "5154 Rob Huebel, Rob Corddry, Ken Marino, Erinn Hay... \n", "3106 NaN \n", "\n", " names_and_roles \\\n", "19 Donald Faison (Dr. Christopher Duncan Turk), J... \n", "5804 Nicola Correia-Damude (Dr. Vanessa Banks), Cat... \n", "3271 Jo Jung Suk (Ik Joon), Kim Dae Myung (Suk Hyun... \n", "4097 Lauren Ambrose (Susan Wheeler), Natalie Knepp ... \n", "1837 Marc Balaguer (Toni), Igor Szpakowski (Jordi),... \n", "3476 Mamie Gummer (Emily Owens), Aja Naomi King (Ca... \n", "714 Matt Czuchry (Dr. Conrad Hawkins), Manish Daya... \n", "4074 Александр Новиков (Юра), Валентина Ляпина (Джу... \n", "5154 Rob Huebel (Dr. Owen Maestro / Rob Huebel), Ro... \n", "3106 Не указаны \n", "\n", " genre dates country \\\n", "19 Комедия, Драма, Медицинский 02.10.2001 — 17.03.2010 США \n", "5804 Драма, Медицинский 06.01.2020 — 29.08.2021 Канада \n", "3271 Драма, Медицинский 12.03.2020 — 16.09.2021 Южная Корея \n", "4097 Детектив, Драма, Фантастика 03.09.2012 — 04.09.2012 США \n", "1837 Драма 24.01.2011 — 22.04.2013 Испания \n", "3476 Комедия, Драма, Романтика 16.10.2012 — 05.02.2013 США \n", "714 Драма, Медицинский, Триллер 21.01.2018 — 17.01.2023 США \n", "4074 Драма, Триллер 31.05.2021 — 14.09.2022 Россия \n", "5154 Комедия, Медицинский 11.07.2010 — 15.04.2016 США \n", "3106 Комедия, Медицинский, Романтика 27.01.2024 — 17.03.2024 Южная Корея \n", "\n", " imdb kinopoisk_rating annotation_len \\\n", "19 8.4 8.731 46 \n", "5804 6.1 NaN 61 \n", "3271 8.8 8.780 15 \n", "4097 5.8 6.277 24 \n", "1837 8.1 8.287 50 \n", "3476 7.5 7.058 136 \n", "714 7.8 7.610 108 \n", "4074 5.9 6.356 65 \n", "5154 7.8 6.066 42 \n", "3106 7.5 7.890 23 \n", "\n", " clean_description cosine_similarity \n", "19 отучившись четыре года в медицинской школе джо... 0.754829 \n", "5804 сюжет сериала разворачивается в торонто где уч... 0.720963 \n", "3271 истории врачей и медсестёр которые окончили од... 0.700698 \n", "4097 молодая студенткамедик обнаруживает что чтото ... 0.699154 \n", "1837 по воле злого рока несколько подростков попада... 0.688771 \n", "3476 наконецто эмили оуэнс чувствует что повзрослел... 0.673646 \n", "714 молодой врачординатор девон правеш начинает ка... 0.656689 \n", "4074 пятеро друзей юра джулс лика крест и костя з... 0.639900 \n", "5154 больница не место для лентяев больница это м... 0.639729 \n", "3106 два гениальных доктора которые в школе соперни... 0.633453 " ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Пример пользовательского запроса\n", "user_query = \"Молодые специалисты после школы пришли в больницу\"\n", "top_k_results = search_series(user_query)\n", "top_k_results\n", "\n", "#0.594781\n", "#0.810437 - evclid" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# Сохранение эмбеддингов\n", "np.save('combined_embeddings_2.npy', description_embeddings)\n", "# np.save('actors_embeddings_2.npy', actors_embeddings)\n", "# np.save('genre_embeddings_2.npy', genre_embeddings)\n", "\n", "# Сохранение FAISS индекса\n", "faiss.write_index(index, 'faiss_index_2.bin')" ] }, { "cell_type": "markdown", "metadata": { "id": "U95tQgekfQgP" }, "source": [ "# RuBERT - нужна другая предобработка" ] }, { "cell_type": "code", "execution_count": 227, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 209, "referenced_widgets": [ "07fda446240344c68ad0d559fb54cf13", "c0296b23f78b4340adf50ca9857cd426", "33c2289b69954e02ab3e7128f8770c09", "1028f226d14545fe9849f0bd4dfa7e25", "2157a5768441485fb4894e8f2bf3aaea", "32c8d3de05c24d288116ff352bd3ecf0", "048f7ffd30a344e1a58b143b20fa3797", "15d2e06f338e4d3c8ad8b0ac61884bc8", "33627097332d4818ba1d98d6578e195f", "c4543b670b9041d8b4e989b38e346562", "aa4ee3d5334d42f192b08dfa54f528e3", "42cad56a5bd34776bcce2edaf000d67c", "8dd20cd23a3f496cab4cbea76ff1b968", "b271bba9c24b466ca43e623734388cb6", "66472847614046aab47e9add4c4b3b64", "10c71be4ec3d4e5fb30f8131d06dc255", "67825b8d9d1c45bbbd45dbd271f720b1", "080460db5c1947e4b646fd5c9589dd42", "37e1238427c24283b1204d49dca43327", "c786cd2b3c914f35a2df3e19b7a512c4", "05b17562395d48fc9693a78ad5471d26", "f4447620a3f443148335a391bacc648f", "e1cc02fff1114c0886d90b5fe77ee6ff", "2fa2521fe5f14cd79f24a0825fa95f01", "64be0f47c68842f29e864b750a6c68e2", "b4a25a91dcf24d6285b29bf3bea3c120", "a6d01dca756a41a4bf304e7e07e18359", "3611dd55773247ed9208dda16f0a604d", "4ce30be0a9d74176bc7a653b2d128d8b", "da103fa6dff34f7791ab22a21ba52b4f", "db590471efa5443ba2cb34f86b7126e1", "1a0d1b2f4410423fb667f2cb61ea1e2f", "84f53479fce0409d9f9f80b2704c4ff4", "a485c2a2871241598e40f7f1b63fb02f", "c43dcb6845494f0c83fcfbdd23306dea", "eecef0e76b114971acf53a3b6ebf4c5c", "da45ab4eb26d4efda812ae216562fe2e", "70bc6cd6a62f468dbe640bab8ae5c387", "e0347b7bb06c450ab736a59d9ff81f11", "0309c25ddf6b4fb2b257da9de004884f", "724cf1de721b47ae8e7fd4f099ff7916", "21861218825f4b158197540f2ec713ce", "b3ac07368bd3497c89ee8fc96351d43c", "243f40e8fe8848bf93772a73b34c23c5", "d731239c6ac44d7f8f5507f1d49e77d4", "e460a92d295141e48c97f34f1e049c99", "ecf8265173244e409acf69c6e45dc198", "ccb4a161c5624eba870bbc374c5d03d1", "03222debe0bf444199ce0b5ed3413500", "06079b682b604bc5a1ff521c6c52b2b2", "4b28178e79404ea9920071040852febb", "a5e91f614e114010821c32f10b1020a8", "ecec111e15974ce983fc1a6dcd101855", "969e3690dff141148a93725107f9abed", "bfd2a98f52484852940074832ad0eeb2", "036dddca3ba14fe594edf6b44571c1c3", "5f3f298c9c644313a03dca73a752e571", "d5644da5b1c64b65be527da8b6d5ca00", "e6adc5a77616483c81ff35a4b4f5686f", "86b668d8f7e443ec95dbea323d2ea032", "8348954e7aba4bafab3e30d93b58e199", "95f0f0b1e92742c5b23f660d4017a18a", "00f8c7e0447d478ab9031ecc0dbc43d1", "9237cc88b94c4aa6a080d5ec41a7c539", "68cc516323584fb0b309577ae1ce861c", "3d12d21c4f514f54b7c9e78f48741766" ] }, "id": "6K88lJPjfQgP", "outputId": "2186aed7-fc9a-4115-e1c0-32437ee1daf8" }, "outputs": [], "source": [ "import torch\n", "from transformers import AutoTokenizer, AutoModel\n", "name = \"cointegrated/rubert-tiny2\"\n", "tokenizer = AutoTokenizer.from_pretrained(name)\n", "model = AutoModel.from_pretrained(name)" ] }, { "cell_type": "code", "execution_count": 228, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 200, "referenced_widgets": [ "7655418afb0b4d37af316ac4595c85b8", "3083796a008248f28a578cc10969ec5f", "6a0f1bebd74a4da28dc81709c85507dd", "23f5422f3b604b219a84693498d38028", "0a835cb98fa2424f8a9720f26b18fa60", "31209c25672d4faf981a72d95a859c57", "c4bafa640b3548718e35949c9f7747b5", "b2ce8e555c8a4b75948335e3bc2639a4", "040e793d5c264e03b325b1da7b63fa40", "ffe008b397c54a4f97bc297a97771bb5", "7a54cba7025f41fda6b5f3eb524c8d61", "f3b351e8213b417d8cc5f6a1aaf7a24f", "456dc41011594022be77c1967988b6de", "ec9bbc6453424cdc86f307a6a387ad02", "4c81f843315a471a930c3570b59e751b", "4e06219609b64d4d9d2db3ddcdafc5af", "336f8f769c8744d68123f7b200466d44", "21f30325d4104b19a45cdc6e7706ba3b", "b99e4a92e6cd4ebdb0e47abd3ccb699e", "7a8243a1264f44d799ef3dbf9c2a2909", "9136dc339ef64806bdc1402fb30bd3f2", "2db381fe267d4d2e96502b40684dc17f", "3023f030399547fb9f4866b0ea3df92e", "7c3c2f87765e4e0b9e5f32c13903da79", "86dcd1d4914149ec8d868a182bfe42b5", "3d5178881bda42008c2c95e58f9fe83e", "c3d4215ef3e444ab9412a1f62b6f384f", "9de57d33c4bf4d75876b11be3b0075e7", "eaf9e0bef5364fbaadbd056ebb8f7cdd", "611fe16caca34a7ea3fabbe6ae0fa9f8", "23dfdc30e6c24d7293cc8bbdecb70771", "e8ee4a8870d74357b594c2d5a6d21032", "5ecb8018461f4d62b0e538b2ba8657fb", "1462a2c7d4004a01a0a4c16da773a389", "45389b1811da4d9fbfed25eb27bc93d7", "e080da81678242ebb70d9806daa3dd5f", "8df2680052fc462488c80118e5a7da13", "a62f0625f618491d8fc60fb31c2e7186", "f775cad86e90491aababc37d35308c9f", "015b64f49edf455c80eb64bfcb6db06e", "4cb14e591d7148b6ac20d0c516ad7ee5", "4fb7a72385b243d4a14b612be8a00c23", "0b1fc51fe21d4ebabbde5af03583b3c0", "66ac8d09099647d19b54c8e2ca73f9ce" ] }, "id": "ryF02Mf9fQgP", "outputId": "cc988d98-a8f3-43cc-9e0f-e863f9be4841" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/vera/miniforge3/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", " warnings.warn(\n" ] } ], "source": [ "model = SentenceTransformer(name) # Используем RuBERT для семантического поиска\n", "descriptions = data['description'].tolist()\n", "description_embeddings = model.encode(descriptions, convert_to_tensor=True).cpu().numpy()\n", "\n", "# Нормализация векторов для косинусного сходства\n", "description_embeddings = description_embeddings / np.linalg.norm(description_embeddings, axis=1, keepdims=True)\n", "\n", "# Создание индекса с использованием Faiss для косинусного сходства\n", "dimension = description_embeddings.shape[1]\n", "index = faiss.IndexFlatIP(dimension)\n", "index.add(description_embeddings)\n" ] }, { "cell_type": "code", "execution_count": 229, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "8gG50x1LfQgP", "outputId": "48816b2f-7dff-407b-dcd0-85e48c274fdf" }, "outputs": [ { "data": { "text/html": [ "
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titlelinkdescriptionannotation_lencosine_similarity
7510Клятва любвиhttps://myshows.me/view/74588/отца линь чжисяо талантливой виолончелистки го...330.709849
3182Мэри убивает людейhttps://myshows.me/view/51989/доктор мэри харрис матьодиночка и врач скорой ...550.698886
1851Бесстыдникиhttps://myshows.me/view/810/жизнь детей из многодетной семьи оставленной м...130.698359
4353Раскосяченныеhttps://myshows.me/view/53374/конопляная легенда рут уайтфезер фельдман нани...270.697979
2364Кровьhttps://myshows.me/view/42175/пак джи сан красив богат умен заведующий отдел...300.696770
7320Боб любит Абишолуhttps://myshows.me/view/62313/продавец носков из детройта боб пережил инфарк...220.695213
4563Пьяная фирмаhttps://myshows.me/view/51654/похмельный сериал про гениального врача которы...160.695000
5446Дэцкая больницаhttps://myshows.me/view/7676/больница не место для лентяев больница это м...420.691805
2062Санка Рэаhttps://myshows.me/view/24059/фуруя сын священника одержим зомби он коллекци...530.688429
1878Алхимия душhttps://myshows.me/view/79093/чан ук мужчина из респектабельной семьи котор...230.687544
\n", "
" ], "text/plain": [ " title link \\\n", "7510 Клятва любви https://myshows.me/view/74588/ \n", "3182 Мэри убивает людей https://myshows.me/view/51989/ \n", "1851 Бесстыдники https://myshows.me/view/810/ \n", "4353 Раскосяченные https://myshows.me/view/53374/ \n", "2364 Кровь https://myshows.me/view/42175/ \n", "7320 Боб любит Абишолу https://myshows.me/view/62313/ \n", "4563 Пьяная фирма https://myshows.me/view/51654/ \n", "5446 Дэцкая больница https://myshows.me/view/7676/ \n", "2062 Санка Рэа https://myshows.me/view/24059/ \n", "1878 Алхимия душ https://myshows.me/view/79093/ \n", "\n", " description annotation_len \\\n", "7510 отца линь чжисяо талантливой виолончелистки го... 33 \n", "3182 доктор мэри харрис матьодиночка и врач скорой ... 55 \n", "1851 жизнь детей из многодетной семьи оставленной м... 13 \n", "4353 конопляная легенда рут уайтфезер фельдман нани... 27 \n", "2364 пак джи сан красив богат умен заведующий отдел... 30 \n", "7320 продавец носков из детройта боб пережил инфарк... 22 \n", "4563 похмельный сериал про гениального врача которы... 16 \n", "5446 больница не место для лентяев больница это м... 42 \n", "2062 фуруя сын священника одержим зомби он коллекци... 53 \n", "1878 чан ук мужчина из респектабельной семьи котор... 23 \n", "\n", " cosine_similarity \n", "7510 0.709849 \n", "3182 0.698886 \n", "1851 0.698359 \n", "4353 0.697979 \n", "2364 0.696770 \n", "7320 0.695213 \n", "4563 0.695000 \n", "5446 0.691805 \n", "2062 0.688429 \n", "1878 0.687544 " ] }, "execution_count": 229, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Шаг 4: Обработка пользовательского запроса и асимметричный поиск\n", "def search_series(user_query, top_k=10):\n", " # user_query = clean_text(user_query)\n", " user_query = (user_query)\n", " query_embedding = model.encode([user_query], convert_to_tensor=True).cpu().numpy()\n", " query_embedding = query_embedding / np.linalg.norm(query_embedding, axis=1, keepdims=True) # Нормализация\n", " D, I = index.search(query_embedding, top_k)\n", " results = data.iloc[I[0]].copy()\n", " cosine_similarities = D[0]\n", " results['cosine_similarity'] = cosine_similarities\n", " return results\n", "\n", "# Пример пользовательского запроса\n", "user_query = \"Учитель, больной раком, варит метамфетамин, чтобы оставить своей семье денег\"\n", "top_k_results = search_series(user_query)\n", "top_k_results" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "00f8c7e0447d478ab9031ecc0dbc43d1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": 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