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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: kaleido\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "lida 0.0.10 requires fastapi, which is not installed.\n", + "lida 0.0.10 requires python-multipart, which is not installed.\n", + "lida 0.0.10 requires uvicorn, which is not installed.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed kaleido-0.2.1\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "vWPUBx6u6A2u" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.cluster import KMeans\n", + "from sklearn import metrics\n", + "from sklearn.manifold import TSNE\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from laser_encoders import LaserEncoderPipeline\n", + "\n", + "import datasets\n", + "from datasets import load_dataset\n", + "\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Loading the MASSIVE Dataset\n", + "We'll use the MASSIVE dataset, which includes texts in 52 languages. Let's load a subset of this dataset for our experiment:\n", + "\n" + ], + "metadata": { + "id": "tFlfrpM3nTuA" + } + }, + { + "cell_type": "code", + "source": [ + "from datasets import load_dataset\n", + "\n", + "n = 20\n", + "eng_data = load_dataset(\"AmazonScience/massive\", \"en-US\", split=f'train[:{n}]')\n", + "fra_data = load_dataset(\"AmazonScience/massive\", \"fr-FR\", split=f'train[:{n}]')\n", + "ita_data = load_dataset(\"AmazonScience/massive\", \"it-IT\", split=f'train[:{n}]')\n", + "spa_data = load_dataset(\"AmazonScience/massive\", \"es-ES\", split=f'train[:{n}]')\n", + "jap_data = load_dataset(\"AmazonScience/massive\", \"ja-JP\", split=f'train[:{n}]')\n", + "arb_data = load_dataset(\"AmazonScience/massive\", \"ar-SA\", split=f'train[:{n}]')\n", + "chn_data = load_dataset(\"AmazonScience/massive\", \"zh-CN\", split=f'train[:{n}]')\n", + "afr_data = load_dataset(\"AmazonScience/massive\", \"af-ZA\", split=f'train[:{n}]')\n", + "rus_data = load_dataset(\"AmazonScience/massive\", \"ru-RU\", split=f'train[:{n}]')\n", + "hin_data = load_dataset(\"AmazonScience/massive\", \"hi-IN\", split=f'train[:{n}]')" + ], + "metadata": { + "id": "zTQQWUVmSMaI", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "ef2284aa8320437c96e8d9944472eff8", + "f4e55157596449cabdcda9cdfeed3337", + "6825f43d731b43938b4756027cf00b90", + "419fd003df5e424d853a662a7fbcf163", + "6817f5f2a151455cb9a34b454b9b3b65", + "2d3a918a6aed462caf3d631b13293052", + "d9a4e9e897534e56a84df7267d169ba7", + "3d894e42d3be4de4a2699e33024e60d7", + "f1d3c3d323d34d46bbd653c5ea9b6bbe", + "cdcb47f39bd548729d833f7bee66bd53", + "4729e9e5ca474f009c956d536e42ffd8", + "4feaf4d2af394763a411a0eed8f98d21", + "74ee527e66ae4da8aa22ac8ba86dbff8", + "264048f8d3354c0a9b52e0cd1a9196d6", + "97ddef844af341c4b5cb3d5258179b4d", + "300c325405794db28cfdf08cece1ae7f", + "4792cf57014f4be6abd7001b0c572d42", + "76b79afdd3924a6eb712945fba050144", + 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