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(setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=f6d2b92ec4edbd0abe39d8144e36ae512cf707af23ef553dacfc8769591b44c2\n", + " Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n", + "Successfully built ffmpy\n", + "Installing collected packages: pydub, ffmpy, websockets, semantic-version, python-multipart, orjson, markdown-it-py, h11, aiofiles, uvicorn, starlette, mdit-py-plugins, httpcore, httpx, fastapi, gradio-client, gradio\n", + " Attempting uninstall: markdown-it-py\n", + " Found existing installation: markdown-it-py 3.0.0\n", + " Uninstalling markdown-it-py-3.0.0:\n", + " Successfully uninstalled markdown-it-py-3.0.0\n", + " Attempting uninstall: mdit-py-plugins\n", + " Found existing installation: mdit-py-plugins 0.4.0\n", + " Uninstalling mdit-py-plugins-0.4.0:\n", + " Successfully uninstalled mdit-py-plugins-0.4.0\n", + "Successfully installed aiofiles-23.2.1 fastapi-0.101.1 ffmpy-0.3.1 gradio-3.40.1 gradio-client-0.4.0 h11-0.14.0 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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