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httpcore-0.17.3 httpx-0.24.1 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 orjson-3.9.4 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.27.0 uvicorn-0.23.2 websockets-11.0.3\n" ] } ] }, { "cell_type": "code", "source": [ "import warnings\n", "warnings.filterwarnings('ignore')" ], "metadata": { "id": "KrWFRhcIwlr2" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QWd5xNcZPSwf", "outputId": "af0865f0-1c83-47cd-c2cd-38887bf8bfa8" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] } ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "1y2hQdL9v2H2" }, "outputs": [], "source": [ "#import all reqiured package\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import re\n", "import torch\n", "import random\n", "import torch.nn as nn\n", "import transformers\n", "from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup\n", "import matplotlib.pyplot as plt\n", "from torch.utils.data import Dataset, DataLoader\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix, classification_report\n", "from collections import defaultdict\n", "import pickle\n", "from tqdm import tqdm\n", "import gradio as gr" ] }, { "cell_type": "code", "source": [ "# specify GPU\n", "device = torch.device(\"cuda\")" ], "metadata": { "id": "Z1B3lorSPnOg" }, "execution_count": 5, "outputs": [] }, { "cell_type": "markdown", "source": [ "The code employs the BERT language model for breaking down text into tokens and translating them into numerical IDs. Initially, the chosen BERT model, 'bert-base-cased', is set, and a tokenizer is initialized accordingly.\n", "\n", "Subsequently, the script takes a sample text, \"originally gave this a 2 star,\" and processes it through the tokenizer. This procedure involves transforming the text into a sequence of tokens, which are the fundamental units that BERT comprehends." ], "metadata": { "id": "1-Kk65MVPpHW" } }, { "cell_type": "code", "source": [ "MODEL_NAME = 'bert-base-cased'\n", "tokenizer = transformers.BertTokenizer.from_pretrained(MODEL_NAME)\n", "\n", "sample_text = \"originally gave this a 2 star\"\n", "\n", "tokens = tokenizer.tokenize(sample_text)\n", "ids = tokenizer.convert_tokens_to_ids(tokens)\n", "print(f'{sample_text}')\n", "print('='*60)\n", "print(tokens)\n", "print('='*60)\n", "print(ids)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 200, "referenced_widgets": [ "b4fc2c984f2645018de746f5d8a797ce", "e7d1f445ef554aed8822694d1f8359da", "ee98241475ba45b286f06419a5e6f98e", "a26e3b24701b404c9a4ecfe5254e80a7", "5653f8a132284815ba6024eaa630bb99", "cdcda9f0bd3f46c7a4bdfded45c5a337", "92ae8f417c194689b37737c202beb775", "26927005165346aa88afe05c526ffbd5", "8cf6d499de9240caac68361f47c827c2", "8e375f57951945f8b30a3221feb1c321", "44a626e31a174c0999eb69c020c73ccf", "4f9828add59f42d88df8e5e05bca6d35", "8f95fd57907c4535bd85368ffe919cee", "108684ab8406424fa36a50fd9e83963f", "9c312b06a55c4e92a6bd849f7eb26c1b", "3493bc8db73146d2ae94de22e1a10cbb", "5e9670b2fb3b46499154c27d3a22c30e", "1911a60f265a4f20919b7331b18d0a65", "bf9441545b514803af65596ed8e1ccf3", "d8f6b777fd3b4c4484d51fcdc63548d4", "cfcc1efafcd64f67a6eb04697e6a2e36", "ab8d32d5b535443eae20205157a71632", "90034728af564f3cb3eee515f39d7e14", "367b25f9e63a45c2bcfcc115512fa229", "9ed3184b1e374bd69f427dd000d0c194", "00b55aa69c05491e8cf8a8d450d76ca7", "d951bdcf4b0146a8abde000546a8aaf6", "42c2016023204f16a694e5f7ba74ccb2", "9bf04a48ab8e4552957f37af2a171760", "08015c83e858438e93e6f294b96e2ac5", "04f35e81be6648bf962eaae1b95e7c8b", "105eb4205ede46c5b80884f5b523c182", "5a4590edde474f68951201b1a6e21f3d" ] }, "id": "I_MkvDNtv-CL", "outputId": "e821d206-3607-4bf5-b85b-05e158760d1a" }, "execution_count": 6, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)solve/main/vocab.txt: 0%| | 0.00/213k [00:00" ], "text/html": [ "
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