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July 23, 2021 21:01
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phrase_similarity
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| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/avidale/05797b4c3d437f830d261d3c36fe9801/phrase_similarity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "B8LRh0gmHR3N" | |
| }, | |
| "source": [ | |
| "Этот блокнот показывает, как выполнить классификацию коротких текстов по небольшому числу примеров, используя предобученную нейросеть. \n", | |
| "\n", | |
| "Работает это так:\n", | |
| "1. Нейросеть переводит каждый текст в вектор. Она [обучалась](https://habr.com/ru/post/562064/) это делать так, что у текстов, похожих по смыслу, и векторы похожие. \n", | |
| "2. Мы сравниваем новый текст с векторами текстов-примеров, и для каждого интента выводим максимальное векторное сходство нового текста с примерами в составе этого интента. " | |
| ] | |
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| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "s53dL0rnFuWv" | |
| }, | |
| "source": [ | |
| "!pip install sentencepiece transformers" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ASBhOfcoGBbq" | |
| }, | |
| "source": [ | |
| "Выбираем модель, которую хотим использовать. \n", | |
| "\n", | |
| "https://huggingface.co/cointegrated/rubert-tiny - туповатая, но очень быстрая\n", | |
| "\n", | |
| "https://huggingface.co/cointegrated/LaBSE-en-ru - умнее, но больше и медленнее" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "iRbYqdesFyQL" | |
| }, | |
| "source": [ | |
| "MODEL_NAME = 'cointegrated/rubert-tiny'" | |
| ], | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "tKv-X5cBHPc6" | |
| }, | |
| "source": [ | |
| "Скачиваем модель" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 284, | |
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| }, | |
| "id": "xLiyRSAHGf0n", | |
| "outputId": "daf4b292-34a0-4fc5-9a9a-bc254ced735e" | |
| }, | |
| "source": [ | |
| "from transformers import AutoModel, AutoTokenizer\n", | |
| "model = AutoModel.from_pretrained(MODEL_NAME)\n", | |
| "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)" | |
| ], | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Some weights of the model checkpoint at cointegrated/rubert-tiny were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias']\n", | |
| "- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" | |
| ], | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=341.0, style=ProgressStyle(description_…" | |
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| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "953dc956581c414785cf6f013c04d3cd", | |
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| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "6cee4d068c99493d9d699ccfff14ffd3", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=112.0, style=ProgressStyle(description_…" | |
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| }, | |
| "metadata": { | |
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| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "-IFz2p2rJPxo" | |
| }, | |
| "source": [ | |
| "Пишем функцию для перевода текста в числовой вектор. Главная магия - тут!" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "yMAQvzPAGAZt" | |
| }, | |
| "source": [ | |
| "import numpy as np\n", | |
| "import torch\n", | |
| "\n", | |
| "def embed_bert_cls(text, model, tokenizer):\n", | |
| " t = tokenizer(text, padding=True, truncation=True, max_length=128, return_tensors='pt')\n", | |
| " t = {k: v.to(model.device) for k, v in t.items()}\n", | |
| " with torch.no_grad():\n", | |
| " model_output = model(**t)\n", | |
| " embeddings = model_output.last_hidden_state[:, 0, :]\n", | |
| " embeddings = torch.nn.functional.normalize(embeddings)\n", | |
| " return embeddings[0].cpu().numpy()" | |
| ], | |
| "execution_count": 6, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "GcFQRLlNJTs5" | |
| }, | |
| "source": [ | |
| "Записываем наш словарик текстов из разных интентов, которые мы хотим уметь определять" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "yfVuclAJGRem" | |
| }, | |
| "source": [ | |
| "intents = {\n", | |
| " 'how_are_you': ['как дела', 'как поживаешь'],\n", | |
| " 'toast': ['я поднимаю стакан', 'пью до дна', 'за ваше здоровье'],\n", | |
| " 'music': ['включи музыку', 'вруби шансон', 'исполните мне пожалуйста симфонию'],\n", | |
| "}" | |
| ], | |
| "execution_count": 7, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "S19MNrDGJZZy" | |
| }, | |
| "source": [ | |
| "Переводим все эти тексты в векторы, чтобы с ними сравниваться" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "rHX23QcCHEpH" | |
| }, | |
| "source": [ | |
| "example_vectors = []\n", | |
| "intent_names = []\n", | |
| "for intent, texts in intents.items():\n", | |
| " for text in texts:\n", | |
| " example_vectors.append(embed_bert_cls(text, model, tokenizer))\n", | |
| " intent_names.append(intent)\n", | |
| "example_vectors = np.stack(example_vectors)" | |
| ], | |
| "execution_count": 9, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "XNFOkm6pJqjt", | |
| "outputId": "045200bd-b3f4-48b9-f5cc-2755fbe96fc3" | |
| }, | |
| "source": [ | |
| "example_vectors.shape" | |
| ], | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(8, 312)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 10 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "jqyucHDxJel8" | |
| }, | |
| "source": [ | |
| "Пишем функцию для сравнения текста с обучающими примерами" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "UnDHiRHxIxq9" | |
| }, | |
| "source": [ | |
| "from collections import Counter\n", | |
| "\n", | |
| "def classify_text(text):\n", | |
| " vector = embed_bert_cls(text, model, tokenizer)\n", | |
| " scores = np.dot(example_vectors, vector)\n", | |
| " result = Counter()\n", | |
| " for score, intent in zip(scores, intent_names):\n", | |
| " result[intent] = max(result[intent], score)\n", | |
| " return result" | |
| ], | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "S4GeMapYK1iu" | |
| }, | |
| "source": [ | |
| "Теперь можно вписывать любой свой текст и смотреть на оценки сходства с каждым интентом" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "ggw0D_ujJ5Uj", | |
| "outputId": "1bfb8e63-6c82-40f1-8b2a-457174aa7a9f" | |
| }, | |
| "source": [ | |
| "text = 'стакан я поднимаю'\n", | |
| "print(classify_text(text).most_common())" | |
| ], | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "[('toast', 0.9790392), ('music', 0.6900815), ('how_are_you', 0.65599346)]\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "6qSdfBl8KSO1", | |
| "outputId": "c8b33e13-043d-42f5-acd0-c1d0aaa2b645" | |
| }, | |
| "source": [ | |
| "text = 'подымаю чашу!'\n", | |
| "print(classify_text(text).most_common())" | |
| ], | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "[('toast', 0.78018653), ('how_are_you', 0.72003293), ('music', 0.6865895)]\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "w12XjvMbK7Z0", | |
| "outputId": "1caf4fbd-935f-4fde-fdb1-c165b580c10a" | |
| }, | |
| "source": [ | |
| "text = 'как пройти в библиотеку?'\n", | |
| "print(classify_text(text).most_common())" | |
| ], | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "[('how_are_you', 0.66733), ('toast', 0.52488065), ('music', 0.51675415)]\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Bwu12Sb-KJeZ" | |
| }, | |
| "source": [ | |
| "Можно замерить время выполнения функции, это 10-15 мс" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "XgnSgnyDJ_S5", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "8716fb26-9347-4d31-8fd0-acebcf6c396b" | |
| }, | |
| "source": [ | |
| "%%time\n", | |
| "text = 'стакан я поднимаю'\n", | |
| "\n", | |
| "print(classify_text(text).most_common())" | |
| ], | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "[('toast', 0.9790392), ('music', 0.6900815), ('how_are_you', 0.65599346)]\n", | |
| "CPU times: user 7.92 ms, sys: 843 µs, total: 8.76 ms\n", | |
| "Wall time: 11.2 ms\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "zVcduq-PKIrC" | |
| }, | |
| "source": [ | |
| "" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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