{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "m-XmgG_KqSkj", "outputId": "06fee69a-7c0f-4fcc-d554-466f12176a4e" }, "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": "code", "execution_count": null, "metadata": { "id": "PF1aq9rUsc1v" }, "outputs": [], "source": [ "import os\n", "from bs4 import BeautifulSoup\n", "\n", "data_folder = '/content/drive/My Drive/Data/HUISGENOOT/'\n", "\n", "number_files = 0\n", "\n", "corpus = \"\"\n", "\n", "# Continuing from 51 - 500\n", "corpus = \"\"\n", "\n", "for filename in os.listdir(data_folder):\n", " if filename.endswith(\".html\"):\n", " number_files += 1\n", " if number_files > 50 and number_files <= 500: #documents from 51 to 500\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 == 500: # Stop at 500 documents\n", " break\n", "\n", "# Append the new documents to the existing corpus file\n", "with open('/content/drive/My Drive/Data/HUISGENOOT_corpus_51_550.txt', 'a', encoding='utf-8') as file:\n", " file.write(corpus)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "02EwcA5YtLOQ" }, "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": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "IuqzV6mRtYYh", "outputId": "114f420c-2e2c-459d-c1b8-224c25e33d59" }, "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": "UedNtivStevn" }, "outputs": [], "source": [ "import nltk\n", "nltk.data.path.append(\"'/content/drive/My Drive/Data/Stopw\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "u3_ZJxoMtoci" }, "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_after_main.txt', 'w', encoding='utf-8') as file:\n", " file.write(filtered_text)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "fc46cc2d406840f8974dcd7a9562be4c", "9aa482075b6c485e9645a2e7ae7ecda6", "a441de8530f941c4b901e202dd301e3a", "dbf2770b397042b38afe52229462c39e", 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" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=7524, training_loss=3.062664554703432, metrics={'train_runtime': 1370.0659, 'train_samples_per_second': 21.962, 'train_steps_per_second': 5.492, 'total_flos': 1965569310720000.0, 'train_loss': 3.062664554703432, 'epoch': 3.0})" ] }, "metadata": {}, "execution_count": 7 } ], "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_51_550.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()" ] }, { "cell_type": "code", "source": [ "# Save the trained model\n", "output_dir = \"./gpt2-afrikaans\"\n", "model.save_pretrained(output_dir)\n", "tokenizer.save_pretrained(output_dir)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, 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