diff --git "a/notebooks/Finetuning_MiniLM (1).ipynb" "b/notebooks/Finetuning_MiniLM (1).ipynb" new file mode 100644--- /dev/null +++ "b/notebooks/Finetuning_MiniLM (1).ipynb" @@ -0,0 +1,24471 @@ +{ + "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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