Bert_SLU.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_CoX8Ajymovd"
      },
      "outputs": [],
      "source": [
        "!pip install accelerate -U\n",
        "!pip install transformers soundfile torchmetrics gdown"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "CymJ6TaMBcEM"
      },
      "outputs": [],
      "source": [
        "!mkdir ./dataset\n",
        "import gdown\n",
        "def drive_download(idx, output):\n",
        "    url = 'https://drive.google.com/uc?id=' + idx\n",
        "    gdown.download(url, output, quiet=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uEV1_u8wmrGv",
        "outputId": "10b219ea-0250-4a14-bf83-3030cac69d6f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1ZBL3h6bHMmd8MIUNXqg72PucUkC9ZSWJ\n",
            "To: /content/dataset/train_data.zip\n",
            "100%|██████████| 733M/733M [00:06<00:00, 106MB/s] \n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1ZepptsTrVSjQEx-dpBBmQ2b7xYFLn_64\n",
            "To: /content/dataset/public_test.zip\n",
            "100%|██████████| 131M/131M [00:01<00:00, 72.1MB/s]\n"
          ]
        }
      ],
      "source": [
        "# Data download\n",
        "drive_download(\"1ZBL3h6bHMmd8MIUNXqg72PucUkC9ZSWJ\", \"./dataset/train_data.zip\")\n",
        "drive_download(\"1ZepptsTrVSjQEx-dpBBmQ2b7xYFLn_64\", \"./dataset/public_test.zip\")\n",
        "# drive_download(\"1K_07kix1OgBGO2FNPh-Lxqr1yLbtqFYt\", \"./dataset/train.jsonl\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZJZDIHlnBeFT"
      },
      "outputs": [],
      "source": [
        "!unzip ./dataset/public_test.zip -d ./dataset/test\n",
        "!unzip ./dataset/train_data.zip -d ./dataset/train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RLOT8SkPm3Gk",
        "outputId": "bebff642-4f68-447e-9207-2376831b32ba"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=186Tv-dPED5QiIJy4sRvlsNsvYxLpXfWX\n",
            "To: /content/vn_base_vocab.json\n",
            "100%|██████████| 1.35k/1.35k [00:00<00:00, 5.00MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1KXn1A17ce7jNX1qQhx8l0DQK1KYhbmls\n",
            "To: /content/train_token_labels_20230909.json\n",
            "100%|██████████| 271k/271k [00:00<00:00, 3.62MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1K-oNwBu2svshAkmifU9wISKPMvKgeKy4\n",
            "To: /content/train_20230909.jsonl\n",
            "100%|██████████| 3.30M/3.30M [00:00<00:00, 21.0MB/s]\n"
          ]
        }
      ],
      "source": [
        "# Data vocab, sentence, and token label\n",
        "# drive_download(\"186Tv-dPED5QiIJy4sRvlsNsvYxLpXfWX\", \"./vn_base_vocab.json\")\n",
        "# drive_download(\"1KXn1A17ce7jNX1qQhx8l0DQK1KYhbmls\", \"./train_token_labels_20230909.json\")\n",
        "# drive_download(\"1K-oNwBu2svshAkmifU9wISKPMvKgeKy4\", \"./train_20230909.jsonl\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QDoosxEknlZu"
      },
      "outputs": [],
      "source": [
        "#  Upload py file: model.py, utils.py, dataset.py, trainer.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "tpqlqXOGnCLU"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import utils\n",
        "from trainer import Trainer\n",
        "from model import BertSLU\n",
        "from functools import partial\n",
        "from dataset import BertDataset\n",
        "from torch.utils.data import DataLoader\n",
        "from transformers import AutoTokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "eN73cW1enEKT"
      },
      "outputs": [],
      "source": [
        "def custom_collate(tokenizer, is_train, batch):\n",
        "    inputs = tokenizer([i[\"text\"] for i in batch], return_tensors=\"pt\", padding=\"longest\")\n",
        "    if not is_train:\n",
        "        return inputs, torch.zeros_like(inputs[\"input_ids\"]), torch.zeros(inputs[\"input_ids\"].size(0))\n",
        "    seq_len = inputs[\"input_ids\"].size(1)\n",
        "    token_labels = torch.stack([\n",
        "        torch.tensor(i[\"token_label\"] + [-100]*(seq_len - len(i[\"token_label\"]))) for i in batch\n",
        "    ])\n",
        "    intent_labels = torch.tensor([i[\"intent_label\"] for i in batch])\n",
        "    return inputs, token_labels, intent_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "6mVloO5enEMw"
      },
      "outputs": [],
      "source": [
        "def get_loader(annotation_path, token_label_path, batch_size=2, test_size=0.3):\n",
        "    tokenizer = AutoTokenizer.from_pretrained(\"vinai/phobert-base\")\n",
        "    annotations = utils.load_annotation(annotation_path)\n",
        "    all_intent = [i[\"intent\"] for i in annotations]\n",
        "    all_text = [i[\"sentence\"] for i in annotations]\n",
        "    all_label = utils.load_json(token_label_path)\n",
        "    all_label = [all_label, all_intent]\n",
        "    dataset = BertDataset(all_text, all_label, utils.MAP_INTENT)\n",
        "    N = len(dataset)\n",
        "    train_size = int(N * (1-test_size))\n",
        "    train_set, valid_set = torch.utils.data.random_split(dataset, [train_size, N-train_size])\n",
        "    train_loader = DataLoader(\n",
        "        dataset,\n",
        "        batch_size=batch_size,\n",
        "        shuffle=True,\n",
        "        collate_fn=partial(custom_collate, tokenizer, True)\n",
        "    )\n",
        "    valid_loader = DataLoader(\n",
        "        valid_set,\n",
        "        batch_size=batch_size,\n",
        "        collate_fn=partial(custom_collate, tokenizer, True)\n",
        "    )\n",
        "    return train_loader, valid_loader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "a7nbfvJU63f5"
      },
      "outputs": [],
      "source": [
        "def get_test_loader(test_path, batch_size=2):\n",
        "    tokenizer = AutoTokenizer.from_pretrained(\"vinai/phobert-base\")\n",
        "    sequences = utils.load_json(test_path)\n",
        "    id_seqs = [k for k, v in sequences.items()]\n",
        "    seqs = [v for k, v in sequences.items()]\n",
        "    dataset = BertDataset(seqs)\n",
        "    test_loader = DataLoader(\n",
        "        dataset,\n",
        "        batch_size=batch_size,\n",
        "        shuffle=False,\n",
        "        collate_fn=partial(custom_collate, tokenizer, False)\n",
        "    )\n",
        "    return test_loader, id_seqs, seqs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQ4DbGdjnEPR",
        "outputId": "bd44adaa-6230-4754-c482-cbfd199e2bb9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Len train_loader: 469 - Len valid_loader: 141\n"
          ]
        }
      ],
      "source": [
        "train_loader, valid_loader = get_loader(\"./train_20230909.jsonl\", \"./train_token_labels_20230909.json\", 16)\n",
        "print(f\"Len train_loader: {len(train_loader)} - Len valid_loader: {len(valid_loader)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148,
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          ]
        },
        "id": "lbAISnQTBqEW",
        "outputId": "c950bc59-7862-4804-de54-3ca10eb606d8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf6fe53a5b5c4268ac8c342c14340588",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/557 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0892632053af456fad6a1e219a28528f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/895k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1fee3e01147d4d3f98311facdf0a8d8c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)solve/main/bpe.codes:   0%|          | 0.00/1.14M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "163"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_loader, test_file_id, all_seqs = get_test_loader(\"./w2v_lasted_test_sentences.json\", 8)\n",
        "len(test_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "PfOxuR5nnEHy"
      },
      "outputs": [],
      "source": [
        "class config:\n",
        "    epochs = 20\n",
        "    checkpoint_path = \"./checkpoint/checkpoint_bert.pt\"\n",
        "    learning_rate = 5e-5\n",
        "    adam_eps = 1e-8\n",
        "    warmup_steps = 1000\n",
        "    weight_decay = 0.005"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hrz_jhkRnERs",
        "outputId": "11cc9ea6-ddfd-4ded-f637-30f5e0675631"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of RobertaModel were not initialized from the model checkpoint at vinai/phobert-base-v2 and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num of param: 135016728\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = BertSLU(15, 9, \"vinai/phobert-base-v2\")\n",
        "print(f\"Num of param:\", sum(p.numel() for p in model.parameters()))\n",
        "optimizer = torch.optim.AdamW(utils.weight_decay(model, config.weight_decay), lr=config.learning_rate, eps=config.adam_eps)\n",
        "criterion = torch.nn.CrossEntropyLoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NlZ6Hulkp9zt"
      },
      "outputs": [],
      "source": [
        "!mkdir ./checkpoint"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-knZKV8BnEUE"
      },
      "outputs": [],
      "source": [
        "trainer = Trainer(model, optimizer, criterion, amp=True, device=device)\n",
        "# trainer.load_checkpoint(\"./checkpoint/checkpoint_bert.pt\") Load checkpoint if wanna continue training\n",
        "trainer.fit(train_loader, valid_loader, epochs=config.epochs, checkpoint=config.checkpoint_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upR889pM2INL"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ubTk8891gdeX"
      },
      "outputs": [],
      "source": [
        "drive_download(\"1ZBpjMakFSDdShWbn1_BHstr--wRCnlv7\", \"./checkpoint_bert.pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "hUiLWtzAgdri"
      },
      "outputs": [],
      "source": [
        "!cp -r ./drive/MyDrive/datasets/checkpoint_bert.pt ./"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hEhRLDy7Cnhi",
        "outputId": "76051cc8-e3e0-4ce5-f811-ef73dbfc6e43"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[+] Model load successful\n",
            " 163 / 163\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(1299, 1299)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer = Trainer(model, optimizer, criterion, amp=True, device=device)\n",
        "trainer.load_checkpoint(\"./checkpoint_bert.pt\")\n",
        "all_tokens, all_intents = trainer.test(test_loader)\n",
        "len(all_tokens), len(all_intents)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EW5JCiDBFWsA",
        "outputId": "e7956de0-de99-44ab-aca3-c035cb03ea0f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"vinai/phobert-base\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "92ZSv_lXJR0l"
      },
      "outputs": [],
      "source": [
        "INVERSE_MAP_TOKENS = {\n",
        "    0: 'word',\n",
        "    1: 'time at',\n",
        "    2: 'device',\n",
        "    3: 'changing value',\n",
        "    4: 'scene',\n",
        "    5: 'command',\n",
        "    6: 'location',\n",
        "    7: 'duration',\n",
        "    8: 'target number'\n",
        " }\n",
        "\n",
        "INVERSE_MAP_INTENTS = {\n",
        "    0: 'Giảm độ sáng của thiết bị',\n",
        "    1: 'Đóng thiết bị',\n",
        "    2: 'Hủy hoạt cảnh',\n",
        "    3: 'Tắt thiết bị',\n",
        "    4: 'Tăng âm lượng của thiết bị',\n",
        "    5: 'Giảm mức độ của thiết bị',\n",
        "    6: 'Bật thiết bị',\n",
        "    7: 'Tăng mức độ của thiết bị',\n",
        "    8: 'Tăng nhiệt độ của thiết bị',\n",
        "    9: 'Kiểm tra tình trạng thiết bị',\n",
        "    10: 'Mở thiết bị',\n",
        "    11: 'Giảm âm lượng của thiết bị',\n",
        "    12: 'Kích hoạt cảnh',\n",
        "    13: 'Giảm nhiệt độ của thiết bị',\n",
        "    14: 'Tăng độ sáng của thiết bị'\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "p-QhP_ztFwzn"
      },
      "outputs": [],
      "source": [
        "def collect_label(token):\n",
        "    token = token[1:]\n",
        "    for i in range(len(token) - 1, -1, -1):\n",
        "        if token[i] != 0:\n",
        "            token = token[:i+1]\n",
        "            break\n",
        "    token += [-1]\n",
        "    map_labels = []\n",
        "    cur = 0\n",
        "    val = token[0]\n",
        "    for idx, i in enumerate(token[1:], 1):\n",
        "        if i == val:\n",
        "            continue\n",
        "        else:\n",
        "            if val != 0:\n",
        "                map_labels.append([cur, idx-1, val])\n",
        "            val = i\n",
        "            cur = idx\n",
        "    return map_labels\n",
        "\n",
        "def convert_into_output(all_tokens, all_intents, all_seqs, test_file_id, tokenizer):\n",
        "    ans = []\n",
        "    for idx in range(len(all_tokens)):\n",
        "        token = all_tokens[idx]\n",
        "        intent = all_intents[idx]\n",
        "        seq = tokenizer.tokenize(all_seqs[idx])\n",
        "        labels = collect_label(token)\n",
        "        tmp_ans = {\n",
        "            \"intent\": INVERSE_MAP_INTENTS[intent],\n",
        "            \"file\": test_file_id[idx]\n",
        "        }\n",
        "        entities = []\n",
        "        # print(labels)\n",
        "        # print(seq)\n",
        "        # return\n",
        "        for label in labels:\n",
        "            if label[-1] == 0:\n",
        "                continue\n",
        "            sub_text = seq[label[0]: label[1]+1]\n",
        "            sub_text = tokenizer.decode(\n",
        "                tokenizer.convert_tokens_to_ids(sub_text), skip_special_tokens=True\n",
        "            )\n",
        "            tmp_add = {\"type\": INVERSE_MAP_TOKENS[label[-1]], \"filler\": sub_text}\n",
        "            # check = list(filter(lambda x: tmp_add[\"type\"] == x[\"type\"] and tmp_add[\"filler\"] == x[\"filler\"], entities))\n",
        "            # if len(check):\n",
        "                # continue\n",
        "            entities += [tmp_add]\n",
        "        tmp_ans[\"entities\"] = entities\n",
        "        ans.append(tmp_ans)\n",
        "        print(\"\\r\", end=\"\")\n",
        "        print(f\"\\r {idx+1} / {len(all_tokens)}\", end=\"\")\n",
        "    return ans"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_COeQeYzINjD",
        "outputId": "4eec98df-a2d8-421c-abb1-648746b25b0c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " 1299 / 1299"
          ]
        }
      ],
      "source": [
        "ans = convert_into_output(all_tokens, all_intents, all_seqs, test_file_id, tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-fBarcgXLXnr",
        "outputId": "465f7134-8a37-482a-a7a3-1695537b1ce7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'intent': 'Tăng độ sáng của thiết bị',\n",
              "  'file': 'gkr2nW4Zxwv9ay6iR1od5jP.wav',\n",
              "  'entities': [{'type': 'command', 'filler': 'tăng'},\n",
              "   {'type': 'device', 'filler': 'đèn hắt'},\n",
              "   {'type': 'target number', 'filler': '8%'}]},\n",
              " {'intent': 'Tắt thiết bị',\n",
              "  'file': 'WoGuEH1SVfdNDpDGXxcTbHJ.wav',\n",
              "  'entities': [{'type': 'command', 'filler': 'tắt'},\n",
              "   {'type': 'device', 'filler': 'đèn treo tường'}]},\n",
              " {'intent': 'Mở thiết bị',\n",
              "  'file': 'SDU8HKVUyOmIpvwzWcMgJW7.wav',\n",
              "  'entities': [{'type': 'time at', 'filler': '6 giờ 8 phút'},\n",
              "   {'type': 'command', 'filler': 'mở'},\n",
              "   {'type': 'device', 'filler': 'máy sưởi'}]},\n",
              " {'intent': 'Đóng thiết bị',\n",
              "  'file': 'QaiOJwzIYKRxVrLDQHxODfn.wav',\n",
              "  'entities': [{'type': 'command', 'filler': 'đóng'},\n",
              "   {'type': 'device', 'filler': 'lò nướng'}]},\n",
              " {'intent': 'Bật thiết bị',\n",
              "  'file': 'vda3D3tnIOiwHHelu3W7PJM.wav',\n",
              "  'entities': [{'type': 'command', 'filler': 'bật'},\n",
              "   {'type': 'device', 'filler': 'đèn bếd'}]}]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ans[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "HvN-RjVNL_T7"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "with open(\"./predictions.jsonl\", \"w\", encoding=\"utf-8\") as f:\n",
        "    for line in ans:\n",
        "        json.dump(line, f)\n",
        "        f.write('\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
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        },
        "id": "OWsB4vdu8Op6",
        "outputId": "94d228dc-f296-4259-c27d-1f4a85a20b75"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tBMr_GrpEVpH"
      },
      "outputs": [],
      "source": [
        "!cp -r ./checkpoint/checkpoint_bert.pt ./drive/MyDrive/datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_N-3adxegs7n"
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