{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "YhCKJjdUAINq" }, "source": [ "# **Google Drive mounting**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JFFB4N2i_eTS", "outputId": "9c5c3edf-645b-4c67-b2ce-d0e422abdf71" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "markdown", "metadata": { "id": "Xpo8hUWzAdLw" }, "source": [ "# **Data Processing**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "sXFqlySeAxlz" }, "outputs": [], "source": [ "import os\n", "from bs4 import BeautifulSoup\n", "data_folder = '/content/drive/My Drive/Data/HUISGENOOT/'\n", "corpus = \"\"\n", "number_files = 0 # Initialize number_files to 0\n", "\n", "for filename in os.listdir(data_folder):\n", " if filename.endswith(\".html\"):\n", " number_files += 1\n", " if number_files > 1000 and number_files <= 1800: # documents from 1001 to 1800\n", " with open(os.path.join(data_folder, filename), 'r', encoding='utf-8') as file:\n", " html_content = file.read()\n", " soup = BeautifulSoup(html_content, 'html.parser')\n", " text = soup.get_text()\n", " corpus += text + \"\\n\"\n", "\n", " if number_files == 1800: # Stop at 1000 documents\n", " break\n", "\n", "# Save the processed text to a file\n", "with open('/content/drive/My Drive/Data/HUISGENOOT_corpus_1001_1800.txt', 'w', encoding='utf-8') as file:\n", " file.write(corpus)" ] }, { "cell_type": "markdown", "metadata": { "id": "SxXkL_RzDQhH" }, "source": [ "# Data Cleaning" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1jC7MXNGDV_M" }, "outputs": [], "source": [ "import re\n", "\n", "def clean_text(text):\n", " # Remove newlines, extra spaces, and non-breaking spaces\n", " text = text.replace('\\n', ' ').replace('\\xa0', ' ').strip()\n", "\n", " # Remove special characters and numbers using regex\n", " text = re.sub(r'[^a-zA-Z\\s]', '', text)\n", "\n", " # Convert multiple spaces into single space\n", " text = re.sub(r'\\s+', ' ', text)\n", "\n", " return text.lower() # Convert text to lowercase\n", "\n", "# Apply cleaning function to your corpus\n", "cleaned_corpus = clean_text(corpus)" ] }, { "cell_type": "markdown", "metadata": { "id": "FXwEbeqJDa17" }, "source": [ "# Data Tokenization" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "s5xrWgzbDiXa", "outputId": "31c0d099-3ead-4aec-fd50-8e6fd3e24251" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[nltk_data] Downloading package punkt to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt.zip.\n", "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", "[nltk_data] Unzipping corpora/stopwords.zip.\n" ] } ], "source": [ "import nltk\n", "nltk.download('punkt')\n", "nltk.download('stopwords')\n", "from nltk.tokenize import word_tokenize\n", "from nltk.corpus import stopwords" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-wNgcAzHDj5S" }, "outputs": [], "source": [ "import nltk\n", "nltk.data.path.append(\"'/content/drive/My Drive/Data/Stopw\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KYCfNxwGDnjd" }, "outputs": [], "source": [ "# Define Afrikaans stop words\n", "afrikaans_stop_words = set(stopwords.words('dutch'))\n", "\n", "# Tokenize text\n", "tokens = word_tokenize(cleaned_corpus)\n", "\n", "# Remove stop words\n", "filtered_tokens = [word for word in tokens if word.lower() not in afrikaans_stop_words]\n", "\n", "# Join tokens back into a single string\n", "filtered_text = ' '.join(filtered_tokens)\n", "\n", "# Save the preprocessed text to a file\n", "with open('/content/drive/My Drive/Data/HUISGENOOT_corpus_preprocessed_V3.txt', 'w', encoding='utf-8') as file:\n", " file.write(filtered_text)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "TXSTknxSEIGf" }, "source": [ "# Model Training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "36533557d13c4514b514c1086cdaada2", "bb4102e4ac114b66a37655b5d20c507e", "b7afd72512914fca8c3a87d0d816f28e", "581aeb3a111e4ef1afcc6cea5574ca87", "3f193f5a5c82481483165b723b3a45aa", "80e6cbf3855c4c29b1ed031fadd80486", "800e6871a6134faa948d4714197d5439", "c8fd827e93374961862a41aebbda415a", "a876819e14cb43db950089d8f8c4bd70", "e04459c22d854cdeb235176c62d3ee6e", "07549473348e4417bf82e80168f0aa76", "a8c798c31ab24c1f9de247cb9646a7d3", "d9908174d0564f708e7c5ad9e6312658", "99fe87bace0e43e29dd2fcf6e7df6445", "cbd2961edaa34a2fa87a1b967649b441", "4d95c07da7db45eea7f7496a3685c28c", "9f71561d0d984188b92f3a55568cf63e", "5dfb0cc63d6140339812482cb367f845", "dc8228f209ef46dbac4990f0825eab16", "783027b0039d415c90fc5d8ddc5c0221", "2dad9e996b6946b3bd81d3570c87037c", "fb6697ce30c341ea867fc39c0e588e5f", "a51963725250496c9dd849233c8c4ed3", "97b35e8388764e239afe367f8b8440d0", "527d711674af4153b159a48998ecc58b", "158bfe356d3c4cab88d632dc08717522", "4cc6bc4234c8416f8e5990b4602cc853", "265e9438b9d7467aba90aae833fb599b", "ef666f45f4be4a7a8b74eba028d96102", "fb0d8366a1254533aee77a5dbd57ea1c", "0987d00ba041489eb5a9aa9f91ed3b11", "7048a49ac7874e35860182e33b94fa5d", "1268a8d387ff4713aedc68ffa32eee8c", "87b99d3398614fd3a947b70337dc03bc", "6e90fb9d57ca405683997428859ba1a4", "8519e8ee99d04c8d96646553bb30cb57", "4496c862c0f243dbbd95b163b9196a93", "cd32f71d491b4b83a97c727061700e95", "dbb3463ad805450581c718bdd2223867", "c8207b93dae343c9a76799f1f7800ff0", "d0073baf21f244089e9ae7f3f86ac081", "ffb1b50fd5bc452594a7f7579cd0d551", "ad94b43ee7dc4c8d81210e99e56b7568", "337306a52ede46c88b3252ed128e1e0e", "8b15f775242b4279ba5650f9b1bf9072", "85b1dac26652488fb48af1be38ba9e4f", "ee4011df180948d7abaf12ee0371143d", "6f2ab57eba9e42d59d91310f24ce2af2", "0608703233164a6ba053e332f7a630b1", "ac4380628bfc493d8be209d7f1607419", "d2a0921e85f2457abb820da3804ef2f8", "b83738f622954d75a7daa6e08cd614cf", "6e94ad02e849449898ccc8600f4a0095", "e54a87df2aa24534bf932a290269d12d", "45b3ba7e84014abfa89f91c7c91f6f63", "280c71a9f5f140c79b9463ec807e1c4b", "991f4cfc859340e7b7fd130075172f12", "1beef8f20b334e519932cf25ca7e1f93", "dbdc927dbef14ad98e8359a9f867054f", "63876282335d43269fe368859e29e0c5", "00d0266ac8a74b56b4fd1fbb168e9631", "7a98c460178043539b5251c56e34cf54", "aff9f33a8b65428fadda432e53271310", "2680e73cbd11485194d25a8b74caad21", "24fc64c5033b455a9606dcf9f91f01d1", "8803359e8aa94334987e27dfee5df22a", "8552ce906ad9407584b210b26cae6c03", "ea5f4d3ba3f24718a266573af24b382d", "af63c2d055b6441d988e1edaf19f7a74", "8c2f4337d64045d3b53b25441db897e8", "4075c6f48fa8493988993b143f36d6fa", "91a71cf13157477ab8dc9c36c3db7834", "b61e2b81b1ae4d6195b3130456cf4d56", 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" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=8427, training_loss=3.0249779586819154, metrics={'train_runtime': 1550.8083, 'train_samples_per_second': 21.736, 'train_steps_per_second': 5.434, 'total_flos': 2201907953664000.0, 'train_loss': 3.0249779586819154, 'epoch': 3.0})" ] }, "metadata": {}, "execution_count": 8 } ], "source": [ "!pip install transformers\n", "!pip install accelerate>=0.21.0\n", "\n", "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n", "from transformers import TextDataset, DataCollatorForLanguageModeling\n", "from transformers import Trainer, TrainingArguments\n", "\n", "# Load the tokenizer\n", "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n", "\n", "# Tokenize the dataset\n", "train_path = '/content/drive/My Drive/Data/HUISGENOOT_corpus_1001_1800.txt'\n", "train_dataset = TextDataset(\n", " tokenizer=tokenizer,\n", " file_path=train_path,\n", " block_size=128\n", ")\n", "\n", "# Create data collator\n", "data_collator = DataCollatorForLanguageModeling(\n", " tokenizer=tokenizer, mlm=False\n", ")\n", "\n", "# Initialize the model\n", "model = GPT2LMHeadModel.from_pretrained(\"gpt2\")\n", "\n", "# Define the training arguments\n", "training_args = TrainingArguments(\n", " output_dir=\"./gpt2-afrikaans\",\n", " overwrite_output_dir=True,\n", " num_train_epochs=3,\n", " per_device_train_batch_size=4,\n", " save_steps=10_000,\n", " save_total_limit=2,\n", " prediction_loss_only=True,\n", ")\n", "\n", "# Create Trainer instance\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " data_collator=data_collator,\n", " train_dataset=train_dataset,\n", ")\n", "\n", "# Start training\n", "trainer.train()\n" ] }, { "cell_type": "markdown", "source": [ "# Saving" ], "metadata": { "id": "R6rdAqJTPR-I" } }, { "cell_type": "code", "source": [ "# Save the trained model\n", "output_dir = \"./gpt2-afrikaans\"\n", "trainer.save_model(output_dir)\n", "\n", "# 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