Task 1.ipynb

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        "!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\n",
        "\n",
        "\n"
      ],
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      "source": [
        "!pip install pymorphy2\n",
        "!pip install transformers\n",
        "!pip install pytextspan"
      ],
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          "text": [
            "Some weights of the model checkpoint at cointegrated/rubert-tiny were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.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"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from textspan import get_original_spans \n",
        "import nltk\n",
        "from nltk.collocations import *\n",
        "from nltk.metrics.association import QuadgramAssocMeasures\n",
        "\n",
        "import pymorphy2\n",
        "from pymorphy2.tokenizers import simple_word_tokenize\n",
        "from collections import defaultdict, Counter\n",
        "\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModel\n",
        "\n",
        "from string import punctuation\n",
        "\n",
        "tokenizer_bert = AutoTokenizer.from_pretrained(\"cointegrated/rubert-tiny\")\n",
        "model_bert = AutoModel.from_pretrained(\"cointegrated/rubert-tiny\")\n",
        "#model_bert.cuda()  \n",
        "\n",
        "\n",
        "morph = pymorphy2.MorphAnalyzer() \n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Загрузка и предобработка"
      ],
      "metadata": {
        "id": "B7R43rrZ-kYK"
      },
      "id": "B7R43rrZ-kYK"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "20d96f3b",
      "metadata": {
        "id": "20d96f3b"
      },
      "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",
        "train_texts = pd.read_csv('train_split_reviews.txt', delimiter='\\t', names=['text_id','text'])\n",
        "dev_texts = pd.read_csv('dev_reviews.txt', delimiter='\\t', names=['text_id', 'text'])\n",
        "dev_asp = pd.read_csv(\n",
        "    'dev_aspects.txt', \n",
        "    delimiter='\\t', \n",
        "    names=['text_id', 'category', 'mention', 'start', 'end', 'sentiment']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "8237cc2d",
      "metadata": {
        "id": "8237cc2d"
      },
      "outputs": [],
      "source": [
        "def tokenize(text):\n",
        "    tokens = [token.lower() for token in simple_word_tokenize(text)]\n",
        "    return tokens\n",
        "\n",
        "\n",
        "def normalize(text):\n",
        "    tokens = [token.lower() for token in simple_word_tokenize(text)]\n",
        "    text = [morph.parse(token)[0].normal_form for token in tokens]\n",
        "    return tuple(text)\n",
        "\n",
        "\n",
        "def pos_tag(mention):\n",
        "    POS_tag_list = []\n",
        "    for m in mention:\n",
        "        p = morph.parse(m)[0]\n",
        "        POS_tag_list.append(p.tag.POS )\n",
        "    return tuple(POS_tag_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "ce707ba5",
      "metadata": {
        "id": "ce707ba5"
      },
      "outputs": [],
      "source": [
        "train_asp['norm_mention'] = [normalize(m) for m in train_asp['mention']]\n",
        "train_asp['length'] = train_asp['norm_mention'].apply(len)\n",
        "train_asp['pos_tag'] = train_asp['norm_mention'].apply(pos_tag)\n",
        "\n",
        "dev_texts['tokens'] = dev_texts['text'].apply(tokenize)\n",
        "dev_texts['norm_mention'] = dev_texts['text'].apply(normalize)\n",
        "dev_texts['normalized_text'] = dev_texts['norm_mention'].apply(' '.join)\n",
        "dev_texts['length'] = dev_texts['norm_mention'].apply(len)\n",
        "\n",
        "dev_asp['norm_mention'] = dev_asp['mention'].apply(normalize)\n",
        "dev_asp['length'] = dev_asp['norm_mention'].apply(len)\n",
        "dev_asp['pos_tag'] = dev_asp['norm_mention'].apply(pos_tag)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "not_in_train  = set(dev_asp['pos_tag']) - set(train_asp['pos_tag']) #схемы, которых нет в трэин аспектах, но они есть в тестовых\n",
        "pos_tag_cnt = Counter(dev_asp['pos_tag'].to_list()) # сделаем каунтер для тестовых схем: сколько встречается каждая\n",
        "\n",
        "for scheme in not_in_train:\n",
        "    print(scheme, ':', pos_tag_cnt[scheme])   #вот такие схемы в таком количестве мы выкинем фильтрация по постэг схемам трэин аспектов"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkqfR1PGDNdh",
        "outputId": "f37b3870-b0ce-4732-ebd1-fb949b749dea"
      },
      "id": "mkqfR1PGDNdh",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('NOUN', 'ADJF', None, 'ADJF', None) : 1\n",
            "('ADJS', 'PREP', 'NOUN') : 1\n",
            "('NOUN', 'PREP', 'ADVB', None, 'PREP', 'NOUN', None) : 1\n",
            "('PREP', 'INFN') : 1\n",
            "('ADJF', 'NOUN', 'PREP', 'NOUN', 'PREP', 'ADJF') : 1\n",
            "('NOUN', 'GRND') : 1\n",
            "(None, None, None) : 1\n",
            "('INFN', 'NPRO', 'NOUN') : 1\n",
            "('NOUN', 'PREP', 'NUMR', 'NOUN') : 1\n",
            "('INFN', None, 'NOUN') : 1\n",
            "('ADJF', 'NOUN', 'ADJF', 'NOUN') : 1\n",
            "('NOUN', 'CONJ', 'NOUN', 'INFN') : 1\n",
            "('NOUN', 'PREP', 'NOUN', None, 'NOUN', 'CONJ', 'NOUN') : 1\n",
            "('NOUN', 'CONJ', 'NOUN', 'NOUN') : 1\n",
            "('ADJF', 'PREP', 'NOUN') : 1\n",
            "('NOUN', 'NOUN', 'PREP', 'ADJF', 'NOUN') : 1\n",
            "('ADJF', 'NOUN', 'CONJ', 'NOUN') : 1\n",
            "('NOUN', 'NOUN', 'PREP', 'ADJF', 'NOUN', 'CONJ', 'ADJF', 'NOUN') : 1\n",
            "('ADJF', None, 'ADJF', None, 'ADJF', 'NOUN') : 1\n",
            "('INFN', 'NOUN', 'PREP', 'NOUN', 'CONJ', 'NOUN', 'PREP', 'ADJF', 'NOUN', 'CONJ', 'ADJF', 'NOUN') : 1\n",
            "('NOUN', None, None, None) : 2\n",
            "('NOUN', 'PREP', 'ADJF', 'ADJF', 'NOUN') : 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Функции"
      ],
      "metadata": {
        "id": "YkqjJJibzD9o"
      },
      "id": "YkqjJJibzD9o"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### функции для вычислений"
      ],
      "metadata": {
        "id": "Fx9dMQct-8rr"
      },
      "id": "Fx9dMQct-8rr"
    },
    {
      "cell_type": "code",
      "source": [
        "def count_similarity(sim_matrix, train_asp_indexed):\n",
        "    similarity_vec = cosine_similarity(sim_matrix, train_asp_indexed)\n",
        "    return similarity_vec\n",
        "\n",
        "\n",
        "def index_bert(docs):\n",
        "    doc_vecs = []\n",
        "    if type(docs) != list:\n",
        "        docs = [docs] \n",
        "    for doc in docs:\n",
        "        t = tokenizer_bert(doc, padding=True, truncation=True, return_tensors='pt')\n",
        "        with torch.no_grad():\n",
        "            model_output = model_bert(**{k: v.to(model_bert.device) for k, v in t.items()})\n",
        "        embeddings = model_output.last_hidden_state[:, 0, :]\n",
        "        embeddings = torch.nn.functional.normalize(embeddings)\n",
        "        doc_vecs.append(embeddings[0].cpu().numpy())\n",
        "    return np.vstack(doc_vecs) \n",
        "\n",
        "\n",
        "def index_ngrams(ngrams):\n",
        "    ngrams = [' '.join(ngram) for ngram in ngrams]\n",
        "    indexed = index_bert(ngrams)\n",
        "    return indexed\n",
        "\n",
        "\n",
        "def filter_by_cossim(similarity_vec, ngram_found, threshold):\n",
        "    sim_vec = np.squeeze(similarity_vec)\n",
        "    order = np.array(np.squeeze(sim_vec) > threshold)\n",
        "    return ngram_found[order], sim_vec[order]"
      ],
      "metadata": {
        "id": "nSvjyXgl3IEh"
      },
      "id": "nSvjyXgl3IEh",
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Функция для выделения ближайших нграмм"
      ],
      "metadata": {
        "id": "x_kDbGWXXIa3"
      },
      "id": "x_kDbGWXXIa3"
    },
    {
      "cell_type": "code",
      "source": [
        "def find_similar_ngrams(train_asp, n, threshold, dev_texts=None, measures=None, finder=None, topn_assoc=None): #n - ngrams, topn_assoc - сколько берем по ассоциативной метрике, \n",
        "    train_list = train_asp[train_asp.length == n].norm_mention.tolist() #список нграмм из трэин аспектов\n",
        "\n",
        "    if n == 1:\n",
        "        n_grams = []\n",
        "        for text_tokens in dev_texts.norm_mention:\n",
        "            n_grams.extend(tuple([token]) for token in text_tokens)\n",
        "\n",
        "    else:\n",
        "        n_grams = finder.nbest(measures.jaccard, topn_assoc)  #находим нграммы по ассоциативной метрике из тестовых текстов\n",
        "  \n",
        "    filter_n_grams = [ngram for ngram in n_grams if pos_tag(ngram) in set(train_asp['pos_tag'])] #фильтруем по postag схеме\n",
        "\n",
        "    found_ngrams_indexed = index_ngrams(filter_n_grams) #индексируем бертом все найденные нграммы возвращает матрицу размера количество биграм*длину эмбеддингов\n",
        "\n",
        "    top_n_similar = []\n",
        "    one_list = []\n",
        "    values = {}\n",
        "\n",
        "    for ngram in train_list: #идем по аспектным биграммам из трэина\n",
        "\n",
        "        ngram_indexed = index_ngrams([ngram])  #индексируем их - возвращает матрицу размера количество биграм*длину эмбеддингов\n",
        "        rez = count_similarity(found_ngrams_indexed, ngram_indexed) #для биграммы из теста считаем косинусную близость с каждой биграммой из трэин аспектов\n",
        "        rez = np.squeeze(rez, axis=1)  #избавляемся от лишних размерностей\n",
        "        rez = np.expand_dims(rez, axis=0) #добавляем размерность в нужном месте для сортировки\n",
        "        toprez_ngrams, toprez_values = filter_by_cossim(rez, np.array(list(filter_n_grams)), threshold) #сортируем по значению и берем топн ближайших ключей\n",
        "\n",
        "        toprez_list = list(map(tuple, toprez_ngrams))  #убираем размерности и делаем лист из тюплов\n",
        "        top_n_similar.append(toprez_list)\n",
        "        one_list.extend(toprez_list)\n",
        "        \n",
        "\n",
        "        for one, rez in zip(toprez_list, toprez_values): #сделаем словарь: нграмма - максимальная близость к трэин аспектам\n",
        "            if one in values:\n",
        "               if rez > values[one][0]:\n",
        "                    values[one] = [rez, ngram]\n",
        "            else:\n",
        "                values[one] = [rez, ngram]\n",
        "\n",
        "    return values, one_list"
      ],
      "metadata": {
        "id": "D8ZAQ0CgVFbr"
      },
      "id": "D8ZAQ0CgVFbr",
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###функция для выделения категорий"
      ],
      "metadata": {
        "id": "fUooofsiNQs2"
      },
      "id": "fUooofsiNQs2"
    },
    {
      "cell_type": "code",
      "source": [
        "def get_mention_category(data, cat_type, values):\n",
        "    nrgam2cat = {}\n",
        "    mention_categories = data.value_counts(subset=['norm_mention', cat_type])\n",
        "    mention_categories_dict = defaultdict(dict)\n",
        "    for key, value in mention_categories.items():\n",
        "        mention_categories_dict[key[0]][key[1]] = value\n",
        "    most_commons = {k: Counter(v).most_common(1)[0][0] for k, v in mention_categories_dict.items()}\n",
        "    for ngram in values:  #выделенной нграмме\n",
        "        category_definer = values[ngram][1]\n",
        "        nrgam2cat[ngram] = most_commons[category_definer]\n",
        "  \n",
        "    return nrgam2cat"
      ],
      "metadata": {
        "id": "YcIi67mARB4J"
      },
      "id": "YcIi67mARB4J",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Униграммы\n"
      ],
      "metadata": {
        "id": "xvdSw40eX5Qt"
      },
      "id": "xvdSw40eX5Qt"
    },
    {
      "cell_type": "code",
      "source": [
        "values1, one_list1 = find_similar_ngrams(train_asp, 1, 0.999, dev_texts=dev_texts)\n",
        "value_cat1 = get_mention_category(train_asp, 'category', values1)"
      ],
      "metadata": {
        "id": "pP7VjHpQZM9t"
      },
      "id": "pP7VjHpQZM9t",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(value_cat1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1JWHfcpPstEn",
        "outputId": "c1e41470-d283-40f9-ec1d-b17eabe972b0"
      },
      "id": "1JWHfcpPstEn",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "266"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Биграммы"
      ],
      "metadata": {
        "id": "oTRglgARW7ki"
      },
      "id": "oTRglgARW7ki"
    },
    {
      "cell_type": "code",
      "source": [
        "# Коллекция метрик\n",
        "bigram_measures = nltk.collocations.BigramAssocMeasures()\n",
        "\n",
        "ignored = punctuation\n",
        "\n",
        "# Экстрактор коллокаций\n",
        "finder2 = BigramCollocationFinder.from_documents(dev_texts.norm_mention.tolist())\n",
        "finder2.apply_word_filter(lambda w: w.lower() in ignored)\n"
      ],
      "metadata": {
        "id": "XSCFtWgELabQ"
      },
      "id": "XSCFtWgELabQ",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "values2, one_list2 = find_similar_ngrams(train_asp, 2, 0.988, measures=bigram_measures, finder=finder2, topn_assoc=6000)\n",
        "value_cat2 = get_mention_category(train_asp, 'category', values2)"
      ],
      "metadata": {
        "id": "DyvO_fTv4p2R"
      },
      "id": "DyvO_fTv4p2R",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(values2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5dsTNsLEWYNs",
        "outputId": "f6fc4932-9cb8-4f8c-e374-1c7a76a2b76b"
      },
      "id": "5dsTNsLEWYNs",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Триграммы"
      ],
      "metadata": {
        "id": "bZbipJy3mDuG"
      },
      "id": "bZbipJy3mDuG"
    },
    {
      "cell_type": "code",
      "source": [
        "trigram_measures = nltk.collocations.TrigramAssocMeasures()\n",
        "\n",
        "finder3 = TrigramCollocationFinder.from_documents(dev_texts.norm_mention.tolist())\n",
        "finder3.apply_word_filter(lambda w: w.lower() in ignored)"
      ],
      "metadata": {
        "id": "ZoorA7Cjg2mC"
      },
      "id": "ZoorA7Cjg2mC",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "values3, one_list3 = find_similar_ngrams(train_asp, 3, 0.989, measures=trigram_measures, finder=finder3, topn_assoc=8000)\n",
        "value_cat3 = get_mention_category(train_asp, 'category', values3)"
      ],
      "metadata": {
        "id": "DpRr4tKkODei"
      },
      "id": "DpRr4tKkODei",
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(values3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NU6QsV4oLMA",
        "outputId": "91c22e4a-6aa9-46be-9143-67ec2509f821"
      },
      "id": "2NU6QsV4oLMA",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##4-граммы\n"
      ],
      "metadata": {
        "id": "lFPEP3spoSQ5"
      },
      "id": "lFPEP3spoSQ5"
    },
    {
      "cell_type": "code",
      "source": [
        "quadgram_measures = QuadgramAssocMeasures()\n",
        "\n",
        "finder4 = QuadgramCollocationFinder.from_documents(dev_texts.norm_mention.tolist())\n",
        "finder4.apply_word_filter(lambda w: w.lower() in ignored)"
      ],
      "metadata": {
        "id": "1kAJCnWzoLOn"
      },
      "id": "1kAJCnWzoLOn",
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "values4, one_list4 = find_similar_ngrams(train_asp, 4, 0.97, measures=quadgram_measures, finder=finder4, topn_assoc=3980)\n",
        "value_cat4 = get_mention_category(train_asp, 'category', values4)"
      ],
      "metadata": {
        "id": "lvmejKHyosX6"
      },
      "id": "lvmejKHyosX6",
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(values4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JqZkeCHBoskp",
        "outputId": "aac689d2-74a3-4732-becb-69238066fa86"
      },
      "id": "JqZkeCHBoskp",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ищем выделенные n-граммы и формируем итоговый файл"
      ],
      "metadata": {
        "id": "6i_S4_-PcMQS"
      },
      "id": "6i_S4_-PcMQS"
    },
    {
      "cell_type": "code",
      "source": [
        "def get_token_spans(row):\n",
        "  spans = [span[0] for span in get_original_spans(row['tokens'], row['text'])]\n",
        "  return spans\n",
        "\n",
        "\n",
        "def ngram_index(words, ngram, offset):\n",
        "  try:\n",
        "    ind_start = offset + list(nltk.ngrams(words[offset:], len(ngram))).index(tuple(ngram))\n",
        "    ind_end = ind_start + len(ngram) - 1\n",
        "    return (ind_start, ind_end)\n",
        "  except ValueError:\n",
        "    return None\n",
        "\n",
        "def find_text_ngrams(norm_text, ngrams, spans):\n",
        "  ngrams_text = []\n",
        "\n",
        "  for ngram in ngrams:\n",
        "    offset = 0\n",
        "    i = 0\n",
        "    while True and len(norm_text) != offset:\n",
        "      ngram_ind = ngram_index(norm_text, ngram, offset)\n",
        "      if not ngram_ind:\n",
        "        break\n",
        "\n",
        "      if ngram_ind:\n",
        "        first_token_ind = ngram_ind[0]\n",
        "        last_token_ind = ngram_ind[-1]\n",
        "\n",
        "        start = spans[first_token_ind][0]\n",
        "        end = spans[last_token_ind][-1]\n",
        "\n",
        "        offset = last_token_ind + 1\n",
        "\n",
        "        ngrams_text.append((ngram, start, end))\n",
        "\n",
        "  return ngrams_text\n"
      ],
      "metadata": {
        "id": "ZfMD664awE84"
      },
      "id": "ZfMD664awE84",
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dev_texts['spans'] = dev_texts.apply(lambda x: get_token_spans(x), axis=1)\n",
        "total = {**value_cat4, **value_cat2, **value_cat3, **value_cat1}\n",
        "len(total)"
      ],
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        "id": "LpaLMsOkY9rT",
        "outputId": "124d7f8e-a0f9-4a07-a858-80da37e77c30",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "LpaLMsOkY9rT",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "346"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('dev_pred_aspects.txt', 'w') as f:\n",
        "    for tokens, span, id, text in zip(dev_texts.norm_mention.values, dev_texts.spans.values, dev_texts.text_id.values, dev_texts.text):\n",
        "        ngrams_spans = find_text_ngrams(tokens, total.keys(), span)\n",
        "        for ngram, start, end in ngrams_spans:\n",
        "            print(id, total[ngram], text[start:end], start, end, 'neutral', sep=\"\\t\", file=f)"
      ],
      "metadata": {
        "id": "4TShVQNOhy0f"
      },
      "id": "4TShVQNOhy0f",
      "execution_count": 24,
      "outputs": []
    }
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