Task_2.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive"
      ],
      "metadata": {
        "id": "0zKyV9ld-W8-"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/gdrive/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wbz-zsgU_fRy",
        "outputId": "d4fc098e-e331-4871-8fa4-721c0264eb7f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "% cd '/content/gdrive/MyDrive/Colab Notebooks/university'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_DuiGkA4D485",
        "outputId": "8ba49657-e0f2-40c9-823c-197d23e32bde"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/MyDrive/Colab Notebooks/university\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/named-entity/hse-nlp/master/4th_year/Project/train_aspects.txt\n",
        "!wget https://raw.githubusercontent.com/named-entity/hse-nlp/master/4th_year/Project/train_cats.txt\n",
        "!wget https://raw.githubusercontent.com/named-entity/hse-nlp/master/4th_year/Project/train_reviews.txt\n",
        "\n",
        "!wget https://raw.githubusercontent.com/named-entity/hse-nlp/master/4th_year/Project/train_split_aspects.txt\n",
        "!wget https://raw.githubusercontent.com/named-entity/hse-nlp/master/4th_year/Project/train_split_reviews.txt\n",
        "!wget https://raw.githubusercontent.com/named-entity/hse-nlp/master/4th_year/Project/train_split_cats.txt\n",
        "\n",
        "!wget https://raw.githubusercontent.com/named-entity/hse-nlp/master/4th_year/Project/dev_cats.txt\n",
        "!wget https://raw.githubusercontent.com/named-entity/hse-nlp/master/4th_year/Project/dev_reviews.txt\n",
        "!wget https://raw.githubusercontent.com/named-entity/hse-nlp/master/4th_year/Project/dev_aspects.txt"
      ],
      "metadata": {
        "id": "ayPa5_rAolcm"
      },
      "execution_count": null,
      "outputs": []
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            "\u001b[?25h  Building wheel for intervaltree (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h"
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      "source": [
        "!pip install --quiet pytextspan\n",
        "!pip install --quiet natasha\n",
        "!pip install --quiet transformers\n",
        "!pip install --quiet datasets\n",
        "!pip install --quiet pymorphy2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntluwO-WogvD",
        "outputId": "dafe2d16-ab46-4bc3-97e8-e570d863fb6f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "import datasets\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "\n",
        "import nltk\n",
        "from nltk import sent_tokenize\n",
        "import textspan\n",
        "\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "from pymorphy2.tokenizers import simple_word_tokenize\n",
        "\n",
        "from razdel import sentenize\n",
        "from natasha import (\n",
        "    Segmenter,\n",
        "    MorphVocab,\n",
        "    \n",
        "    NewsEmbedding,\n",
        "    NewsMorphTagger,\n",
        "    NewsSyntaxParser,\n",
        "    NewsNERTagger,\n",
        "    \n",
        "    PER,\n",
        "    NamesExtractor,\n",
        "\n",
        "    Doc\n",
        ")\n",
        "\n",
        "\n",
        "import torch\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "from transformers import BertTokenizerFast\n",
        "\n",
        "nltk.download('punkt')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nat = MorphVocab()\n",
        "emb = NewsEmbedding()\n",
        "morph_tagger = NewsMorphTagger(emb)\n",
        "syntax_parser = NewsSyntaxParser(emb)\n",
        "ner_tagger = NewsNERTagger(emb)\n",
        "segmenter = Segmenter()"
      ],
      "metadata": {
        "id": "Hjl046e3snJE"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "metadata": {
        "id": "ZPYBAnsiqajG"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Предобученная на классификацию сентимента модель"
      ],
      "metadata": {
        "id": "ztKI7rSPNQtQ"
      }
    },
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      "source": [
        "tokenizer = BertTokenizerFast.from_pretrained('blanchefort/rubert-base-cased-sentiment-rusentiment')\n",
        "model = AutoModelForSequenceClassification.from_pretrained('blanchefort/rubert-base-cased-sentiment-rusentiment', return_dict=True)\n",
        "model = model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Q8ydJ724ogvH"
      },
      "outputs": [],
      "source": [
        "train_texts = pd.read_csv('train_split_reviews.txt', delimiter='\\t', names=['text_id','text'])\n",
        "\n",
        "dev_texts = pd.read_csv(\n",
        "    'dev_reviews.txt', \n",
        "    delimiter='\\t', \n",
        "    names=['text_id','text']\n",
        ")"
      ]
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              "   text_id                                               text\n",
              "0    25709  Были 1 февраля. Я второй раз, подруга - первый...\n",
              "1    10099  Были в Дитае на прошлой неделе с подругой. Заш...\n",
              "2     1105  Один из моих любимых ресторанов. С самого откр...\n",
              "3     3647  Остались очень довольны. Хотим поблагодарить ш...\n",
              "4    37070  приехали к друзьям в Питер из Москвы на Новый ..."
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "train_texts.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "MbXE9WeLogvJ"
      },
      "outputs": [],
      "source": [
        "train_asp = pd.read_csv(\n",
        "    'train_split_aspects.txt', \n",
        "    delimiter='\\t', \n",
        "    names=['text_id', 'category', 'mention', 'start', 'end', 'sentiment']\n",
        ")\n",
        "\n",
        "dev_asp = pd.read_csv(\n",
        "    'dev_aspects.txt', \n",
        "    delimiter='\\t', \n",
        "    names=['text_id', 'category', 'mention', 'start', 'end', 'sentiment']\n",
        ")\n",
        "\n",
        "dev_pred_asp = pd.read_csv(\n",
        "    'dev_pred_aspects.txt', \n",
        "    delimiter='\\t', \n",
        "    names=['text_id', 'category', 'mention', 'start', 'end', 'sentiment']\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Для простоты сджойним таблицы, каждому mention соответствует его текст, тексты повторяются"
      ],
      "metadata": {
        "id": "SLRWA-0jZ45E"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "PXiDNrCHogvO"
      },
      "outputs": [],
      "source": [
        "full_df = train_texts.join(train_asp.set_index('text_id'), on='text_id', how='left')\n",
        "full_df_dev = dev_texts.join(dev_asp.set_index('text_id'), on='text_id', how='left')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_df_pred = dev_texts.join(dev_pred_asp.set_index('text_id'), on='text_id', how='left')"
      ],
      "metadata": {
        "id": "LpceIgV4IWTd"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dWweLhQbogvP",
        "outputId": "12f79e21-cb2f-4ec7-f3ef-d9a429bfdb29"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "   text_id                                               text  ...  end sentiment\n",
              "0    13823  Зашли в\"аппетит\" случайно.Не смотря на то,что ...  ...  267   neutral\n",
              "0    13823  Зашли в\"аппетит\" случайно.Не смотря на то,что ...  ...  217   neutral\n",
              "0    13823  Зашли в\"аппетит\" случайно.Не смотря на то,что ...  ...  692   neutral\n",
              "0    13823  Зашли в\"аппетит\" случайно.Не смотря на то,что ...  ...  205   neutral\n",
              "0    13823  Зашли в\"аппетит\" случайно.Не смотря на то,что ...  ...  345   neutral\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "full_df_pred.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Для каждого выделенного упоминания находим ближайших синтаксических соседей в одном предложении и попробуем предсказывать на них "
      ],
      "metadata": {
        "id": "2_810UxzNsTe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ew9qk2wzogvR"
      },
      "outputs": [],
      "source": [
        "def get_sentence(text, ment_start, ment_end):\n",
        "    #  выделяем то предожение, к которому относится mention\n",
        "    sents = list(sentenize(text))\n",
        "    for i, span in enumerate(sents):\n",
        "        if span.start <= ment_start < ment_end <= span.stop:\n",
        "            return span.text, span.start"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "0AboJcMqogvR"
      },
      "outputs": [],
      "source": [
        "def get_neighbour(doc, ment_start, ment_end):\n",
        "    dependents = []\n",
        "    head_id = None\n",
        "    head_text = ''\n",
        "    for token in doc.tokens:\n",
        "        if token.start == ment_start or token.stop == ment_end:\n",
        "            id_mention = token.id\n",
        "            head_id = token.head_id\n",
        "            token_text = token.text \n",
        "            break\n",
        "            \n",
        "            \n",
        "    for token in doc.tokens:\n",
        "        #  записываем текстовый вид вершины\n",
        "        if token.id == head_id:\n",
        "            head_text += token.text\n",
        "        #  записываем прямые зависимые упоминания\n",
        "        if token.head_id == id_mention:\n",
        "            dependents.append(token.text)\n",
        "        #  если у вершины (глагола) есть зависимое \"не\", то его тоже включаем в соседей\n",
        "        if token.head_id == head_id and token.text.lower() == 'не':\n",
        "            head_text = 'не ' + head_text\n",
        "        \n",
        "    \n",
        "    if head_text:\n",
        "        res = [head_text, token_text] + dependents\n",
        "    else:\n",
        "        res = [token_text] + dependents\n",
        "\n",
        "    return res"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "0-5SQD6togvS"
      },
      "outputs": [],
      "source": [
        "def get_nearest_synt_neighbour(row):\n",
        "    text = row['text']\n",
        "    mention = row['mention']\n",
        "    ment_start = row['start']\n",
        "    ment_end = row['end']\n",
        "    sentence, offset = get_sentence(text, ment_start, ment_end)\n",
        "\n",
        "    doc = Doc(sentence)\n",
        "    doc.segment(segmenter)\n",
        "    doc.tag_morph(morph_tagger)\n",
        "    doc.parse_syntax(syntax_parser)\n",
        "\n",
        "    neigh = get_neighbour(doc, ment_start - offset, ment_end - offset)\n",
        "    \n",
        "    return neigh"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "nozv-JpmogvT"
      },
      "outputs": [],
      "source": [
        "full_df['extended_context'] = full_df.apply(lambda x: get_nearest_synt_neighbour(x), axis=1)\n",
        "full_df_dev['extended_context'] = full_df_dev.apply(lambda x: get_nearest_synt_neighbour(x), axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_df_pred['extended_context'] = full_df_pred.apply(lambda x: get_nearest_synt_neighbour(x), axis=1)"
      ],
      "metadata": {
        "id": "Y6UUfl1ZIfC6"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 211,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QN8_7TPuogvT",
        "outputId": "b0a6695c-9a84-4c73-de23-3acac0e1ac4f"
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a3cf1099-3b97-4aca-bf65-8bb83f9e0545 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a3cf1099-3b97-4aca-bf65-8bb83f9e0545');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   text_id  ...                            extended_context\n",
              "0    25709  ...                         [спросили, девушки]\n",
              "0    25709  ...             [прелестях, заведения, данного]\n",
              "0    25709  ...               [диваны, Мягкие, россыпью, .]\n",
              "0    25709  ...                         [россыпью, подушек]\n",
              "0    25709  ...  [музыка, приятная, негромкая, полумрак, .]\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ],
      "source": [
        "full_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Для валидации методов далее будем пытаться предсказывать сентимент для gold standard аспектов на тесте"
      ],
      "metadata": {
        "id": "YFVgnBWlbC5x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Делаем предсказания, где X - это токены аспекта объединенные с его ближайшими синтаксическими соседями, y - сентимент "
      ],
      "metadata": {
        "id": "3PLnRLLtbNj4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "f91znG34ogvT"
      },
      "outputs": [],
      "source": [
        "mapper = {'neutral': 0, 'positive': 1, 'negative': 2, 'both': 0}\n",
        "\n",
        "extracted = list(full_df_dev.extended_context.values)\n",
        "y_true = [mapper[label] for label in full_df_dev.sentiment.values]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ygi1uVE9ogvU",
        "outputId": "886c54b7-7c78-43e4-e575-5a17c54fbdaa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['аппетит', '\"'],\n",
              " ['то', 'встретил', 'случайно', 'нас', 'время'],\n",
              " ['девушка', 'менеджер-'],\n",
              " ['полон', 'девушка', 'менеджер-', 'темноволосая', 'стройная'],\n",
              " ['пришла', 'проводила', 'столу']]"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ],
      "source": [
        "extracted[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "metadata": {
        "id": "QpEzZZJ9ogvV"
      },
      "outputs": [],
      "source": [
        "@torch.no_grad()\n",
        "def predict(text):\n",
        "    inputs = tokenizer(text, max_length=512, padding=True, truncation=True, return_tensors='pt').to(device)\n",
        "    outputs = model(**inputs)\n",
        "    predicted = torch.nn.functional.softmax(outputs.logits, dim=1)\n",
        "    predicted = torch.argmax(predicted, dim=1).cpu().numpy()\n",
        "    return predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {
        "id": "8z9aBQKsogvV"
      },
      "outputs": [],
      "source": [
        "y_pred = [predict(' '.join(text)) for text in extracted]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1oXyhdpiogvW",
        "outputId": "32a3c4c2-59c9-458d-8d39-ff2747a43ce9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.23      0.96      0.37       216\n",
            "           1       0.95      0.31      0.46       795\n",
            "           2       0.80      0.07      0.12       179\n",
            "\n",
            "    accuracy                           0.39      1190\n",
            "   macro avg       0.66      0.44      0.32      1190\n",
            "weighted avg       0.80      0.39      0.39      1190\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_true, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Качество невысокое. Попробуем использовать не только ближайших соседей, а все предложение - с допущением, что в одном предложении редко встречаются противоположные мнения для аспектов."
      ],
      "metadata": {
        "id": "ZrR7W0ucb5E-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "pfVAYj7OogvW"
      },
      "outputs": [],
      "source": [
        "def get_sent(row):\n",
        "    text = row['text']\n",
        "    mention = row['mention']\n",
        "    ment_start = row['start']\n",
        "    ment_end = row['end']\n",
        "    sentence, _ = get_sentence(text, ment_start, ment_end)\n",
        "    \n",
        "    return sentence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XAYMW4gGogvX"
      },
      "outputs": [],
      "source": [
        "full_df['sentence'] = full_df.apply(get_sent, axis=1)\n",
        "full_df_dev['sentence'] = full_df_dev.apply(get_sent, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_df_pred['sentence'] = full_df_pred.apply(get_sent, axis=1)"
      ],
      "metadata": {
        "id": "peblWMQDIqFP"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "metadata": {
        "id": "kC13y0p_ogvX"
      },
      "outputs": [],
      "source": [
        "y_pred_2 = [predict(sentence) for sentence in full_df_dev.sentence]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VR3RCzUlogvY",
        "outputId": "8bc2e52a-06f1-4c1a-ad73-1b747b72f043"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.37      0.73      0.49       216\n",
            "           1       0.88      0.75      0.81       795\n",
            "           2       0.71      0.32      0.44       179\n",
            "\n",
            "    accuracy                           0.68      1190\n",
            "   macro avg       0.65      0.60      0.58      1190\n",
            "weighted avg       0.76      0.68      0.70      1190\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_true, y_pred_2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iUH1rn12ogvc"
      },
      "source": [
        "Классификация сентимента отзывов узкой темы может иметь свои специфические особенности - попробуем дообучить модель на наших трейновых данных (с тем же допущением, что аспект описывает целое предложение)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "3nApLIop0cfR",
        "outputId": "42a9dcd5-3a46-4314-cce4-c98454571410"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>text_id</th>\n",
              "      <th>text</th>\n",
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              "      <th>start</th>\n",
              "      <th>end</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>extended_context</th>\n",
              "      <th>sentence</th>\n",
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              "      <td>[спросили, девушки]</td>\n",
              "      <td>На входе девушки спросили о наших предпочтения...</td>\n",
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              "      <td>Мягкие диваны с россыпью подушек.</td>\n",
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              "      <th>0</th>\n",
              "      <td>25709</td>\n",
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              "      <td>Мягкие диваны с россыпью подушек.</td>\n",
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              "      <td>402</td>\n",
              "      <td>408</td>\n",
              "      <td>positive</td>\n",
              "      <td>[музыка, приятная, негромкая, полумрак, .]</td>\n",
              "      <td>Довольно приятная негромкая музыка и легкий по...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b91059ec-0437-49e8-ad25-64f7b15a8b0d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b91059ec-0437-49e8-ad25-64f7b15a8b0d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   text_id  ...                                           sentence\n",
              "0    25709  ...  На входе девушки спросили о наших предпочтения...\n",
              "0    25709  ...  Предварительно очень подробно рассказав обо вс...\n",
              "0    25709  ...                  Мягкие диваны с россыпью подушек.\n",
              "0    25709  ...                  Мягкие диваны с россыпью подушек.\n",
              "0    25709  ...  Довольно приятная негромкая музыка и легкий по...\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QTbYEaQ4ogvf"
      },
      "outputs": [],
      "source": [
        "train_data = [{'label': mapper[label], 'text': text} for text, label in zip(full_df.sentence.values.tolist(), full_df.sentiment.values.tolist())]\n",
        "test_data = [{'label': mapper[label], 'text': text} for text, label in zip(full_df_dev.sentence.values.tolist(), full_df_dev.sentiment.values.tolist())]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "elXyTLzoogvf"
      },
      "outputs": [],
      "source": [
        "df_train = pd.DataFrame(train_data)\n",
        "df_test = pd.DataFrame(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1YbIs00pogvf"
      },
      "outputs": [],
      "source": [
        "dataset_train = datasets.Dataset.from_pandas(df_train)\n",
        "dataset_test = datasets.Dataset.from_pandas(df_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "grMX5PP-ogvf"
      },
      "outputs": [],
      "source": [
        "raw_datasets = datasets.DatasetDict({\"train\":dataset_train, \"test\":dataset_test})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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          ]
        },
        "id": "cUnTpZH-ogvg",
        "outputId": "b14945b3-f06b-4852-8467-3778c93edfda"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "74920f68edc941e291069d83e14b466a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "89cbe0ffa7564986819c6a4f45723f31",
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            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"text\"], truncation=True, padding=\"max_length\", max_length=512)\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yqyohUPyogvg"
      },
      "outputs": [],
      "source": [
        "full_train_dataset = tokenized_datasets[\"train\"]\n",
        "full_eval_dataset = tokenized_datasets[\"test\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-cXkQDKZogvg",
        "outputId": "94d31bdf-4951-476f-b983-83a93e518fa5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
          ]
        }
      ],
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\"test_trainer\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KsKDDvSwogvg"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model, args=training_args, train_dataset=full_train_dataset, eval_dataset=full_eval_dataset\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "aBRs3_AHogvh",
        "outputId": "376e60ed-667b-4182-afb5-8a8174b0844e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text.\n",
            "***** Running training *****\n",
            "  Num examples = 3573\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 1341\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1341' max='1341' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1341/1341 43:37, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.721200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.569100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to test_trainer/checkpoint-500\n",
            "Configuration saved in test_trainer/checkpoint-500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-1000\n",
            "Configuration saved in test_trainer/checkpoint-1000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-1000/pytorch_model.bin\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1341, training_loss=0.6045681400853782, metrics={'train_runtime': 2619.0397, 'train_samples_per_second': 4.093, 'train_steps_per_second': 0.512, 'total_flos': 2820312724626432.0, 'train_loss': 0.6045681400853782, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained('finetuned')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rem5VbQIzdUb",
        "outputId": "1d5b5905-2791-4148-b957-6a61a90137f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Configuration saved in fine_tuned/config.json\n",
            "Model weights saved in fine_tuned/pytorch_model.bin\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Предсказание на зафайнтьюненной модели"
      ],
      "metadata": {
        "id": "uNWLTTBkFrQB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_new = AutoModelForSequenceClassification.from_pretrained('./finetuned', return_dict=True)\n",
        "model_new = model_new.to(device)"
      ],
      "metadata": {
        "id": "sVyJNxajEodI"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@torch.no_grad()\n",
        "def predict(text):\n",
        "    inputs = tokenizer(text, max_length=512, padding=True, truncation=True, return_tensors='pt').to(device)\n",
        "    outputs = model_new(**inputs)\n",
        "    predicted = torch.nn.functional.softmax(outputs.logits, dim=1)\n",
        "    predicted = torch.argmax(predicted, dim=1).cpu().numpy()\n",
        "    return predicted"
      ],
      "metadata": {
        "id": "LogYbbji_zny"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = [predict(' '.join(text)) for text in extracted]"
      ],
      "metadata": {
        "id": "C8yh5PF6GGrF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_true, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPOkUeLmHTPi",
        "outputId": "ed24b439-5c1d-4ce2-a50e-2092c8e4b73d"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.34      0.70      0.45       216\n",
            "           1       0.85      0.67      0.75       795\n",
            "           2       0.56      0.34      0.42       179\n",
            "\n",
            "    accuracy                           0.63      1190\n",
            "   macro avg       0.58      0.57      0.54      1190\n",
            "weighted avg       0.71      0.63      0.65      1190\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_2 = [predict(sentence) for sentence in full_df_dev.sentence]"
      ],
      "metadata": {
        "id": "wmToKVZJHXrk"
      },
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_true, y_pred_2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LjhG3DQHtWi",
        "outputId": "0df4b304-2578-45db-db3a-9249c112d989"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.56      0.47      0.51       216\n",
            "           1       0.84      0.91      0.87       795\n",
            "           2       0.66      0.53      0.59       179\n",
            "\n",
            "    accuracy                           0.77      1190\n",
            "   macro avg       0.68      0.64      0.66      1190\n",
            "weighted avg       0.76      0.77      0.76      1190\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Дообучение помогло, лучшие результаты предсказаний оказываются на полных предложениях. Сделаем предсказания на наших выделенных данных и сохраним в файл (отдельно сохраним уверенность модели для каждого предсказания, чтобы использовать это в третьем задании)"
      ],
      "metadata": {
        "id": "5kjQEynxeF5w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "@torch.no_grad()\n",
        "def predict(text):\n",
        "    inputs = tokenizer(text, max_length=512, padding=True, truncation=True, return_tensors='pt').to(device)\n",
        "    outputs = model_new(**inputs)\n",
        "    predicted = torch.nn.functional.softmax(outputs.logits, dim=1)\n",
        "    conf = torch.max(predicted, dim=1)\n",
        "    predicted = torch.argmax(predicted, dim=1).cpu().numpy()\n",
        "    return predicted, conf"
      ],
      "metadata": {
        "id": "DfyyizcKfTRN"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_df_pred.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "FkJCZ30tgMY7",
        "outputId": "50bd7049-c820-40b9-aefb-0d96ef599ba0"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th>text_id</th>\n",
              "      <th>text</th>\n",
              "      <th>category</th>\n",
              "      <th>mention</th>\n",
              "      <th>start</th>\n",
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              "      <th>sentence</th>\n",
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              "      <td>671</td>\n",
              "      <td>692</td>\n",
              "      <td>neutral</td>\n",
              "      <td>[место, качество, и, высокое, обслуживания]</td>\n",
              "      <td>В общем очень хорошее место, разнообразное вку...</td>\n",
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              "      <th>0</th>\n",
              "      <td>13823</td>\n",
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              "      <td>Whole</td>\n",
              "      <td>ресторан</td>\n",
              "      <td>337</td>\n",
              "      <td>345</td>\n",
              "      <td>neutral</td>\n",
              "      <td>[полон, ресторан]</td>\n",
              "      <td>Зашли в\"аппетит\" случайно.Не смотря на то,что ...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "          document.querySelector('#df-0afa6754-fdb6-41bf-aebe-b09040735ce3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0afa6754-fdb6-41bf-aebe-b09040735ce3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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            ],
            "text/plain": [
              "   text_id  ...                                           sentence\n",
              "0    13823  ...  Зашли в\"аппетит\" случайно.Не смотря на то,что ...\n",
              "0    13823  ...  Зашли в\"аппетит\" случайно.Не смотря на то,что ...\n",
              "0    13823  ...  В общем очень хорошее место, разнообразное вку...\n",
              "0    13823  ...  Зашли в\"аппетит\" случайно.Не смотря на то,что ...\n",
              "0    13823  ...  Зашли в\"аппетит\" случайно.Не смотря на то,что ...\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mapper"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ihqjArEHglNZ",
        "outputId": "85d3f163-8280-4dc8-bf3d-7ebafcdbc558"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'both': 0, 'negative': 2, 'neutral': 0, 'positive': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  замапим обратно индексы в текстовые названия sentiment\n",
        "ind2text = {0: 'neutral', 1: 'positive', 2: 'negative'}"
      ],
      "metadata": {
        "id": "2Nh8f0Iogm36"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_3 = [predict(sentence) for sentence in full_df_pred.sentence]"
      ],
      "metadata": {
        "id": "UnOrRjtWhKqf"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_labels = [ind2text[label[0][0]] for label in y_pred_3]"
      ],
      "metadata": {
        "id": "LekPI2sGiS35"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_3[0][1][0].item()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "huapaUIdjBQH",
        "outputId": "f49072a8-c873-4fa7-e403-8ce188be10bf"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6505323648452759"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_confs = [label[1][0].item() for label in y_pred_3]"
      ],
      "metadata": {
        "id": "djJYTI5vi_be"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(pred_confs) == len(pred_labels) == len(full_df_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XqC--90Mjmxc",
        "outputId": "ff9c4a38-97f0-4d96-b6d4-612e67e0ded5"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_df_pred['sentiment'] = pred_labels"
      ],
      "metadata": {
        "id": "ob_CUGOUnJG7"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_df_pred.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "rk5Vcdj3ncu0",
        "outputId": "6964be58-7c6f-448c-eb9d-d51c6de9fa8d"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-ebb6b0c2-9c46-4185-938b-8943fc46b904\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<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_id</th>\n",
              "      <th>text</th>\n",
              "      <th>category</th>\n",
              "      <th>mention</th>\n",
              "      <th>start</th>\n",
              "      <th>end</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>extended_context</th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13823</td>\n",
              "      <td>Зашли в\"аппетит\" случайно.Не смотря на то,что ...</td>\n",
              "      <td>Service</td>\n",
              "      <td>приняла заказ</td>\n",
              "      <td>254</td>\n",
              "      <td>267</td>\n",
              "      <td>positive</td>\n",
              "      <td>[полон, приняла, заказ]</td>\n",
              "      <td>Зашли в\"аппетит\" случайно.Не смотря на то,что ...</td>\n",
              "    </tr>\n",
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              "      <td>Service</td>\n",
              "      <td>дала меню</td>\n",
              "      <td>208</td>\n",
              "      <td>217</td>\n",
              "      <td>positive</td>\n",
              "      <td>[пришла, дала, и, меню]</td>\n",
              "      <td>Зашли в\"аппетит\" случайно.Не смотря на то,что ...</td>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-ebb6b0c2-9c46-4185-938b-8943fc46b904');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ],
            "text/plain": [
              "   text_id  ...                                           sentence\n",
              "0    13823  ...  Зашли в\"аппетит\" случайно.Не смотря на то,что ...\n",
              "0    13823  ...  Зашли в\"аппетит\" случайно.Не смотря на то,что ...\n",
              "\n",
              "[2 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open('confs.pkl', 'wb') as file:\n",
        "  pickle.dump(pred_confs, file)"
      ],
      "metadata": {
        "id": "NHWQx1D4kBfA"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('dev_pred_aspects.txt', 'w') as f:\n",
        "    for text_id, category, mention, start, end, sentiment in zip(full_df_pred.text_id,\n",
        "                                                                full_df_pred.category,\n",
        "                                                                full_df_pred.mention.values, \n",
        "                                                                full_df_pred.start.values, \n",
        "                                                                full_df_pred.end.values,\n",
        "                                                                full_df_pred.sentiment.values,\n",
        "                                                                ):\n",
        "      print(text_id, category, mention, start, end, sentiment, sep=\"\\t\", file=f)"
      ],
      "metadata": {
        "id": "tU5xNXbqYxoc"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
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        ""
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      "outputs": []
    }
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