{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "E9wXgZgXKBH1" }, "source": [ "## a. Introduction:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WrxEs1UL2Ywr", "outputId": "1b85d261-1c8d-4c8c-fd0a-c4317814b610" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "markdown", "metadata": { "id": "XeMyIP55GZuB" }, "source": [ "Earlier, in the DATA CLEANING and EDA Notebook, we looked at how we can clean an NLP DATASET. Well in this section, we will be taking a look at how you can model such a dataset . We will be fine tuning three models:\n", "\n", "- MiniLM from Microsoft\n", "- TinyBeRT from Huawei\n", "- DistillBeRT from HuggingFace.\n", "\n", "Note all these models can be found on HuggingFace.\n", "\n", "Also, the dataset we will be using has already be cleaned. Therefore, you might have to check out the cleaning data set on hwo that was done" ] }, { "cell_type": "markdown", "metadata": { "id": "w7A21o4KGxRY" }, "source": [ "## b.Dependencies" ] }, { "cell_type": "markdown", "metadata": { "id": "mm3jgFitK02g" }, "source": [ "### i. Installations\n", "\n", "> Indented block\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "K31MxohNKbgF" }, "outputs": [], "source": [ "%%capture\n", "!pip install transformers\n", "!pip install accelerate -U\n", "!pip install datasets\n", "!pip install huggingface_hub\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "h4vHoPUvK4Ap" }, "source": [ "### Dependencies Importations" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "L3NIt5VvK-MH", "outputId": "2f70287d-7f29-48f5-9718-f1b908e919f1" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package punkt to /root/nltk_data...\n", "[nltk_data] Package punkt is already up-to-date!\n" ] } ], "source": [ "##for handling path of my datasets\n", "import os\n", "from google.colab import drive\n", "\n", "##for data handling:\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "##for visuals-- just in case\n", "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n", "import torch\n", "from scipy.special import softmax\n", "from transformers import TrainingArguments\n", "from torch import nn\n", "from transformers import Trainer\n", "\n", "\n", "##modelling:\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import classification_report, confusion_matrix, f1_score\n", "import transformers\n", "from transformers import pipeline\n", "from datasets import load_dataset\n", "import nltk\n", "nltk.download('punkt')\n", "##others\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "import os\n", "pd.set_option(\"display.max_rows\", None)\n", "pd.set_option(\"display.max_columns\", None)" ] }, { "cell_type": "markdown", "metadata": { "id": "V7mSJF39LXxB" }, "source": [ "## c. Importing dataset from my Google Drive" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "9ztLUrheMFB9" }, "outputs": [], "source": [ "data_path= '/content/drive/MyDrive/deep-learning/clean_copy.csv'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "k51AAeHrMF0E" }, "outputs": [], "source": [ "data= pd.read_csv(data_path)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "o4fP0f8sMi2d", "outputId": "8bb4168d-a495-4627-be90-b54903c9ee52" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
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main\n", "\n" ] }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/gArthur98/Greg-Sentiment-classifier/commit/dc318d0b68f2c0cb9f9a9ffc97dc7fb5064150ca'" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.push_to_hub()" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0001d77c77274a41aee0b2f7260c8b56": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": 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