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"_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# **Google drive Mounting**" ], "metadata": { "id": "c-EHsBT5py67" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MXE6npfApiZm", "outputId": "781813c8-ef7d-4936-e511-118e3218b184" }, "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", "source": [ "# **Data Processing**" ], "metadata": { "id": "wP6Nk54sqMwX" } }, { "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 > 550 and number_files <= 1000: # documents from 551 to 1000\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 == 1000: # 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_551_1000.txt', 'w', encoding='utf-8') as file:\n", " file.write(corpus)" ], "cell_type": "code", "metadata": { "id": "6H6u2GhWt9Fx" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# **Data Cleaning**" ], "metadata": { "id": "XtiB8jjCuupg" } }, { "cell_type": "code", "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)" ], "metadata": { "id": "7aNlSfMRtjuJ" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# **Data Tokenization**" ], "metadata": { "id": "DAgjhD-Tu-Bs" } }, { "cell_type": "code", "source": [ "import nltk\n", "nltk.download('punkt')\n", "nltk.download('stopwords')\n", "from nltk.tokenize import word_tokenize\n", "from nltk.corpus import stopwords" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "A3srTAf5vEsR", "outputId": "bd632b06-36fa-4a48-ef20-41e8cf220f81" }, "execution_count": null, "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" ] } ] }, { "cell_type": "code", "source": [ "import nltk\n", "nltk.data.path.append(\"'/content/drive/My Drive/Data/Stopw\")" ], "metadata": { "id": "6yrXVrR9vGkH" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "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_V2.txt', 'w', encoding='utf-8') as file:\n", " file.write(filtered_text)\n" ], "metadata": { "id": "A_UpjFkovfts" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Model Training" ], "metadata": { "id": "ULftnYDcwy3T" } }, { "cell_type": "code", "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_551_1000.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" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "d596c891286f428a9579db1d2fe93de7", "0d3e69b4c7da41b6acda70b53f9a1acb", "31e6dc45b70a4c3d829b22dd9d1b9f08", "2996846c01a04eab97474d30fa451b09", "1c3cea92ff6346639aa37aaafe3d333b", "e83c40ee9aa440eba43e41d034b190dc", "94beb841c82e4b96b78b9dd2846bc4bf", 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"stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/26.0 [00:00" ], "text/html": [ "\n", "
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StepTraining Loss
5004.250800
10003.629400
15003.427100
20003.259300
25003.171200
30002.988600
35002.929300
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45002.878700
50002.867300
55002.740400
60002.744300
65002.745800
70002.695500
75002.721500

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=7584, training_loss=3.060651905928986, metrics={'train_runtime': 1472.1274, 'train_samples_per_second': 20.601, 'train_steps_per_second': 5.152, 'total_flos': 1981050863616000.0, 'train_loss': 3.060651905928986, 'epoch': 3.0})" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "markdown", "source": [ "# **Saving **" ], "metadata": { "id": "vujwufLN5NJz" } }, { "cell_type": "code", "source": [ "# Save the trained model\n", "output_dir = \"./gpt2-afrikaans\"\n", "trainer.save_model(output_dir)\n", "\n", "# Save the tokenizer\n", "tokenizer.save_pretrained(output_dir)" ], "metadata": { "id": "LpzfUdVk5Plb", "outputId": "1458f4d0-2471-4adf-f244-12d61f82fba8", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "('./gpt2-afrikaans/tokenizer_config.json',\n", " './gpt2-afrikaans/special_tokens_map.json',\n", " './gpt2-afrikaans/vocab.json',\n", " './gpt2-afrikaans/merges.txt',\n", " './gpt2-afrikaans/added_tokens.json')" ] }, "metadata": {}, "execution_count": 10 } ] } ] }