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| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/Katsumata420/40720652aef56aa9b98d6ca407f34360/jglue-random-baseline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# JGLUE の dev data のランダムベースラインを測定する\n", | |
| "\n", | |
| "LREC paper にラベルの分布が書いてあるのである程度想像できるが、\n", | |
| "ちゃんとスコアの形で出しておくため実施\n", | |
| "https://aclanthology.org/2022.lrec-1.317.pdf\n", | |
| "\n", | |
| "## 測定対象のタスク\n", | |
| "下記の accuracy task\n", | |
| "- jcommonsense\n", | |
| "- marc-ja\n", | |
| "- jnli" | |
| ], | |
| "metadata": { | |
| "id": "5ubG9xaQPGU8" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## ライブラリの用意" | |
| ], | |
| "metadata": { | |
| "id": "r77dZIcpQbFP" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install datasets" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Za8CaxlMQVmP", | |
| "outputId": "a20cb10e-ebd2-40c9-b88b-f1a266f5d7f8" | |
| }, | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", | |
| "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.12.0)\n", | |
| "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.22.4)\n", | |
| "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n", | |
| "Requirement already satisfied: dill<0.3.7,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.6)\n", | |
| "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n", | |
| "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.27.1)\n", | |
| "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.65.0)\n", | |
| "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.2.0)\n", | |
| "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.14)\n", | |
| "Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.4.0)\n", | |
| "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.8.4)\n", | |
| "Requirement already satisfied: huggingface-hub<1.0.0,>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.15.1)\n", | |
| "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.1)\n", | |
| "Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.18.0)\n", | |
| "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0)\n", | |
| "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n", | |
| "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.0.12)\n", | |
| "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n", | |
| "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.2)\n", | |
| "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n", | |
| "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.3)\n", | |
| "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n", | |
| "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (3.12.0)\n", | |
| "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (4.5.0)\n", | |
| "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (1.26.15)\n", | |
| "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n", | |
| "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n", | |
| "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n", | |
| "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2022.7.1)\n", | |
| "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## グローバルなクラスの用意" | |
| ], | |
| "metadata": { | |
| "id": "s8lU7b6UQ2ng" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class AccuracyMetric:\n", | |
| " \"\"\"Accuracy での評価を行う\"\"\"\n", | |
| " def __init__(self):\n", | |
| " pass\n", | |
| "\n", | |
| " def __call__(self, preds: list[int], golds: list[int]) -> float:\n", | |
| " \"\"\"入力された predict と gold から accuracy を計測する\"\"\"\n", | |
| " scores = [self._eval(pred, gold) for pred, gold in zip(preds, golds)]\n", | |
| " return sum(scores) / len(scores)\n", | |
| " \n", | |
| " def _eval(self, pred: int, gold: int) -> int:\n", | |
| " if pred == gold:\n", | |
| " return 1\n", | |
| " else:\n", | |
| " return 0" | |
| ], | |
| "metadata": { | |
| "id": "pOnPWS3BQ467" | |
| }, | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from datasets import DatasetDict, Dataset\n", | |
| "\n", | |
| "class JGlueTask:\n", | |
| " def __init__(self, dataset: DatasetDict, evaluator: AccuracyMetric):\n", | |
| " self.dataset = dataset\n", | |
| " self.evaluator = evaluator\n", | |
| " \n", | |
| " @property\n", | |
| " def dev_data(self) -> Dataset:\n", | |
| " return self.dataset[\"validation\"]\n", | |
| " \n", | |
| " @property\n", | |
| " def n_dev_data(self) -> int:\n", | |
| " return len(self.dataset[\"validation\"])\n", | |
| " \n", | |
| " def _run_random_one_example(self) -> int:\n", | |
| " \"\"\"1事例に対して適当な値をrandomで出力する\"\"\"\n", | |
| " raise NotImplementedError()\n", | |
| " \n", | |
| " def run_random(self) -> float:\n", | |
| " \"\"\"random baseline を dev データ全体に対して算出\n", | |
| " \n", | |
| " \n", | |
| " \"\"\"\n", | |
| " preds = [self._run_random_one_example() for _ in range(self.n_dev_data)]\n", | |
| " golds = [sample[\"label\"] for sample in self.dev_data]\n", | |
| " assert len(preds) == len(golds)\n", | |
| "\n", | |
| " acc = self.evaluator(preds, golds)\n", | |
| " return acc" | |
| ], | |
| "metadata": { | |
| "id": "NALZ5bHWR4-R" | |
| }, | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# random baseline の算出" | |
| ], | |
| "metadata": { | |
| "id": "MJzn4kQ3SpTg" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## import" | |
| ], | |
| "metadata": { | |
| "id": "eKHmXXQTTYeR" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import random\n", | |
| "from datasets import load_dataset\n", | |
| "random.seed(4)" | |
| ], | |
| "metadata": { | |
| "id": "7Cny-xZETUkb" | |
| }, | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## jcommonsense\n", | |
| "\n", | |
| "5択の中から一つ返す" | |
| ], | |
| "metadata": { | |
| "id": "uon5bvanSuLu" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class JcommonsenseQA(JGlueTask):\n", | |
| " def __init__(self, dataset: DatasetDict, evaluator: AccuracyMetric):\n", | |
| " super().__init__(dataset, evaluator)\n", | |
| " self.n_choices = 5\n", | |
| " \n", | |
| " def _run_random_one_example(self) -> int:\n", | |
| " return random.choice(range(self.n_choices))" | |
| ], | |
| "metadata": { | |
| "id": "_WaP8bZuUNYZ" | |
| }, | |
| "execution_count": 5, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "dataset = load_dataset(\"shunk031/JGLUE\", name=\"JCommonsenseQA\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 66, | |
| "referenced_widgets": [ | |
| "6da38f4aa3e24208afe33d4cb58c8a91", | |
| "62966d0390d94c6aa5afccfdda9773ca", | |
| "83c5b61143aa4cc29cb2cb3f9133eae1", | |
| "99839f01409b417ba4b81a4055aa9535", | |
| "7bf57edfcd8e449699fe90207e2fcb32", | |
| "6913edba5e644f98abba67c894a20f46", | |
| "a2c3e21d63614866a17671e49062ec2d", | |
| "b2148d32efff474f83fb20676cbf6e28", | |
| "788c4bfaaaf64e21ae5fcb6e4df17ae3", | |
| "425e0866b1f243f18c70f2e4eab93703", | |
| "63c18108af234d2488844281caa05a10" | |
| ] | |
| }, | |
| "id": "KElEBBy6So-r", | |
| "outputId": "67cd1d10-3804-40d1-f585-025ae9d62875" | |
| }, | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "WARNING:datasets.builder:Found cached dataset jglue (/root/.cache/huggingface/datasets/shunk031___jglue/JCommonsenseQA/1.1.0/8e4f1b751625f2ab105c8d4458377d523d9397bfb22e67f590b346dfe7629d1a)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " 0%| | 0/2 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "6da38f4aa3e24208afe33d4cb58c8a91" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "evaluator = AccuracyMetric()" | |
| ], | |
| "metadata": { | |
| "id": "uolFLwbaTofK" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "task = JcommonsenseQA(dataset, evaluator)" | |
| ], | |
| "metadata": { | |
| "id": "VmCjIWGbXGG-" | |
| }, | |
| "execution_count": 8, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "score = task.run_random()" | |
| ], | |
| "metadata": { | |
| "id": "0DHy8FR9XK_o" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(score)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "IgqUUTjXXS7D", | |
| "outputId": "ff165baa-879f-4fb8-96e4-c02490883b07" | |
| }, | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "0.1876675603217158\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "n_annotations = []\n", | |
| "for label in range(5):\n", | |
| " n_annotation = len([_ for sample in task.dev_data if sample[\"label\"] == label])\n", | |
| " print(n_annotation)\n", | |
| " n_annotations.append(n_annotation)\n", | |
| "print(sum(n_annotations))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "gVCmyuGObgmp", | |
| "outputId": "8f01d01e-759a-4633-db2c-0baccb68f4df" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "216\n", | |
| "237\n", | |
| "240\n", | |
| "228\n", | |
| "198\n", | |
| "1119\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## marc-ja\n", | |
| "2択\n", | |
| "\n", | |
| "- positive: 4,832\n", | |
| "- negative: 822" | |
| ], | |
| "metadata": { | |
| "id": "5R8_n483Yai5" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class MarcJa(JGlueTask):\n", | |
| " def __init__(self, dataset: DatasetDict, evaluator: AccuracyMetric):\n", | |
| " super().__init__(dataset, evaluator)\n", | |
| " self.n_choices = 2\n", | |
| " \n", | |
| " def _run_random_one_example(self) -> int:\n", | |
| " return random.choice(range(self.n_choices))" | |
| ], | |
| "metadata": { | |
| "id": "KRKFysB-Xv5b" | |
| }, | |
| "execution_count": 12, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "dataset = load_dataset(\"shunk031/JGLUE\", name=\"MARC-ja\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 66, | |
| "referenced_widgets": [ | |
| "b08363df00eb40e09faf7f12d65b9ffc", | |
| "02398f2dd28e45c2984c5ec0ac617901", | |
| "8fea283b0e924cd6949130b46f667313", | |
| "c2006a40d11947279542c5d39eb98ebb", | |
| "9356df57ae4641beb2fb01156e54d779", | |
| "f203db45a52149c09966e2d303adba14", | |
| "ba1c1cfa03c64cdaa235deb7447e74e1", | |
| "3a6c2cb524e4459f845e0123ee78f8b3", | |
| "f8d6cd3f3fff4b78b347be1f12f0492f", | |
| "4e4f7ddbc0b14418af6fa0df90590b03", | |
| "a1e92113f98641f6ab051e5b5d7efff2" | |
| ] | |
| }, | |
| "id": "FKdadu_RYmVX", | |
| "outputId": "77fb5617-8e65-47e8-d3b9-72e22523c967" | |
| }, | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "WARNING:datasets.builder:Found cached dataset jglue (/root/.cache/huggingface/datasets/shunk031___jglue/MARC-ja/1.1.0/8e4f1b751625f2ab105c8d4458377d523d9397bfb22e67f590b346dfe7629d1a)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " 0%| | 0/2 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "b08363df00eb40e09faf7f12d65b9ffc" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "evaluator = AccuracyMetric()" | |
| ], | |
| "metadata": { | |
| "id": "elwZEuiwYuN-" | |
| }, | |
| "execution_count": 14, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "task = MarcJa(dataset, evaluator)" | |
| ], | |
| "metadata": { | |
| "id": "CsM0o6KqY2xA" | |
| }, | |
| "execution_count": 15, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "score = task.run_random()" | |
| ], | |
| "metadata": { | |
| "id": "6iUNy0R7Y7_N" | |
| }, | |
| "execution_count": 16, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(score)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "LLMf9hq7ZAeF", | |
| "outputId": "e559aa88-2c95-486e-85f9-282d82c437c4" | |
| }, | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "0.49717014503006723\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## jnli\n", | |
| "3択\n", | |
| "\n", | |
| "- entailment: 353\n", | |
| "- neutral: 1,347\n", | |
| "- contradiction: 734" | |
| ], | |
| "metadata": { | |
| "id": "3Ruxz0pUZCE4" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class Jnli(JGlueTask):\n", | |
| " def __init__(self, dataset: DatasetDict, evaluator: AccuracyMetric):\n", | |
| " super().__init__(dataset, evaluator)\n", | |
| " self.n_choices = 3\n", | |
| " \n", | |
| " def _run_random_one_example(self) -> int:\n", | |
| " return random.choice(range(self.n_choices))" | |
| ], | |
| "metadata": { | |
| "id": "Tr2wDeXkZFy2" | |
| }, | |
| "execution_count": 18, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "dataset = load_dataset(\"shunk031/JGLUE\", name=\"JNLI\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 66, | |
| "referenced_widgets": [ | |
| "d8757223ad5c4379a6eee0a1d7c4479b", | |
| "43992dbb7984493997341ac2b0fa34a9", | |
| "a050acbce6ea42489161a9623e7a982f", | |
| "204d20a05b294e67aa899cf1b46a7051", | |
| "0300d023150c47cc8c9b780774c837dc", | |
| "e030a9c1db254613bc349580a51468a3", | |
| "6713167ca273441bb4b8ef460ad351bf", | |
| "bda5bff99c7947eab3baf518f926cd85", | |
| "59686f156a304b92b3eea317a9a4f744", | |
| "d791e3db8f6040d9afdb1493cd397807", | |
| "17db0e7f9a5b4bff9a0754302fd3db7c" | |
| ] | |
| }, | |
| "id": "sQdGl5GyaKQg", | |
| "outputId": "b0281536-e6fc-47b7-b9a3-8063095a7b9a" | |
| }, | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "WARNING:datasets.builder:Found cached dataset jglue (/root/.cache/huggingface/datasets/shunk031___jglue/JNLI/1.1.0/8e4f1b751625f2ab105c8d4458377d523d9397bfb22e67f590b346dfe7629d1a)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " 0%| | 0/2 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "d8757223ad5c4379a6eee0a1d7c4479b" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "evaluator = AccuracyMetric()" | |
| ], | |
| "metadata": { | |
| "id": "6cp_flLPZMDK" | |
| }, | |
| "execution_count": 20, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "task = Jnli(dataset, evaluator)" | |
| ], | |
| "metadata": { | |
| "id": "_XhWo4iwZPI-" | |
| }, | |
| "execution_count": 21, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "score = task.run_random()" | |
| ], | |
| "metadata": { | |
| "id": "5kpwx3xAZXJy" | |
| }, | |
| "execution_count": 22, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(score)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "qN6WlvaMZYbl", | |
| "outputId": "3f5d3ec9-6dcd-43e2-8146-f6e770ef5ce3" | |
| }, | |
| "execution_count": 23, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "0.3294987674609696\n" | |
| ] | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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