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Eat a balanced and nutritious diet: Make sure your meals are inclusive of a variety of fruits and vegetables, lean protein, whole grains, and healthy fats. This helps to provide your body with the essential nutrients to function at its best and can help prevent chronic diseases.\n", "\n", "2. Engage in regular physical activity: Exercise is crucial for maintaining strong bones, muscles, and cardiovascular health. Aim for at least 150 minutes of moderate aerobic exercise or 75 minutes of vigorous exercise each week.\n", "\n", "3. Get enough sleep: Getting enough quality sleep is crucial for physical and mental well-being. It helps to regulate mood, improve cognitive function, and supports healthy growth and immune function. Aim for 7-9 hours of sleep each night.\n", "What are the three primary colors?\n", "The three primary colors are red, blue, and yellow. These colors are called primary because they cannot be created by mixing other colors and all other colors can be made by combining them in various proportions. In the additive color system, used for light, the primary colors are red, green, and blue (RGB).\n", "Describe the structure of an atom.\n", "\n", "First 5 Arabic sentences:\n", " أعط ثلاث نصائح للبقاء بصحة جيدة.\n", " 1. تناول نظامًا غذائيًا متوازنًا ومغذيًا: تأكد من أن وجباتك تشمل مجموعة متنوعة من الفواكه والخضروات ، والبروتينات الخالية من الدهون ، والحبوب الكاملة ، والدهون الصحية. يساعد ذلك على تزويد جسمك بالعناصر الغذائية الأساسية ليعمل في أفضل حالاته ويمكن أن يساعد في الوقاية من الأمراض المزمنة. 2. انخرط في نشاط بدني منتظم: التمرين ضروري للحفاظ على قوة العظام والعضلات وصحة القلب والأوعية الدموية. استهدف ما لا يقل عن 150 دقيقة من التمارين الهوائية المعتدلة أو 75 دقيقة من التمارين القوية كل أسبوع. 3. الحصول على قسط كافٍ من النوم: الحصول على قسط كافٍ من النوم الجيد أمر بالغ الأهمية للصحة الجسدية والعقلية. يساعد على تنظيم الحالة المزاجية ، وتحسين الوظيفة الإدراكية ، ويدعم النمو الصحي ووظيفة المناعة. اهدف إلى النوم لمدة 7-9 ساعات كل ليلة.\n", " ما هي الألوان الثلاثة الأساسية؟\n", "الألوان الثلاثة الأساسية هي الأحمر والأزرق والأصفر. تسمى هذه الألوان الأساسية لأنه لا يمكن إنشاؤها عن طريق مزج الألوان الأخرى ويمكن صنع كل الألوان الأخرى بدمجها بنسب مختلفة. في نظام الألوان المضافة ، المستخدم للضوء ، تكون الألوان الأساسية هي الأحمر والأخضر والأزرق (RGB).\n", " صف بنية الذرة.\n" ] } ] }, { "cell_type": "code", "source": [ "# Data preparation\n", "def encode_translation_pairs(inputs, outputs, tokenizer, max_len=128):\n", " translations = [\n", " f\"{inputs[i]} <|sep|> {outputs[i]}\"\n", " for i in range(len(inputs))\n", " ]\n", " return tokenizer(\n", " translations,\n", " max_length=max_len,\n", " truncation=True,\n", " padding=\"max_length\",\n", " return_tensors=\"pt\"\n", " )[\"input_ids\"]\n", "\n", "encoded_data = encode_translation_pairs(en_sentences, ar_sentences, tokenizer)\n", "\n", "# Create a Dataset object\n", "dataset = Dataset.from_dict({\"input_ids\": encoded_data, \"labels\": encoded_data})\n", "\n", "# Split datasets\n", "train_size = int(0.8 * len(dataset))\n", "val_size = (len(dataset) - train_size) // 2\n", "train_dataset, temp_dataset = dataset.train_test_split(train_size=train_size).values()\n", "val_dataset, test_dataset = temp_dataset.train_test_split(train_size=val_size).values()" ], "metadata": { "id": "KEz2EDWhgF2n" }, "execution_count": 4, "outputs": [] }, { "cell_type": "code", "source": [ "# Load and configure the model\n", "model = GPT2LMHeadModel.from_pretrained(\"gpt2\")\n", "model.resize_token_embeddings(len(tokenizer))\n", "\n", "# Print the model configuration\n", "print(model.config)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 813, "referenced_widgets": [ "2bea128ff6a84dea98153aa2c398c845", "9860c006ecf841e8ac018927761fadda", "06f1133db9334aa79e927e51220a1561", "f19b60de091948998a0d037896442ac5", "e47071d647b04f4683527fc4c5acb592", "78630ac454e54e91b3b46184da36f29b", "23c4bde6b4cd4b918d8ddc0a504d263d", "821c81b459e04c92a3609ae2c017e1a5", "63aed25f754742948b88a967a1b413d7", "d44d9eaebd4449c39d0a85f8886e3110", "7e4fb57794f9468a9de7a1733a21c0b9", "5a3cde9d5eda48d1bacedb3da7a8c36e", "59dfbf89a4a44856853bbdd20e87c1b6", "35a848f438c44b3aa35bc3ec3720fb6d", "50e96a2a7dac40719b2bfac5aebcd802", "c23fb59092fd4b019a41e6ba82827880", "afbba89a0bfc4a1994e5f5b009f1ff9c", "d1562f9faedd457c9b12c8fe88215a0b", "b92e1b10eba743dea4c248022193de45", "e5703982607e4ccaa875bf168d29b567", "c42451c719a24442aa0eea5d985f5f21", "e01cdc7235bf42ef84a900b1c5bf206e" ] }, "id": "YPkbhpWKhLas", "outputId": "75e529e8-3b80-4336-a367-b314c90f97de" }, "execution_count": 5, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/548M [00:00:26: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n", " trainer = Trainer(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Training started!\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
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EpochTraining LossValidation Loss
12.4456002.390493
22.0914002.346333
31.9710002.335375
41.7707002.391809
51.6386002.447893
61.3529002.500594
71.2986002.558636
81.3506002.575015
91.2493002.616520
101.2534002.634244

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "There were missing keys in the checkpoint model loaded: ['lm_head.weight'].\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Training complete!\n" ] } ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "35r0qHABcmIK", "outputId": "ce27e146-2ef1-41fb-bb65-78aaf761b2ea" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Test Sentence Translations:\n", "English: The cat is sitting on the window.\n", "Arabic: أجد في المقالة وتواسلها من: \"سبع\" = 10/4ر\n", "1) 9-5 cm x 6cm (2). 2)(\n", "\n", "English: I need to buy a new phone.\n", "Arabic: أعد كتابة مثليق المسبوان والره�, \"Budu\". #102625 https://tribalnews3rv4p\n", "\n", "English: She loves reading books during her free time.\n", "Arabic: هذه البانتي إلى مساعة ومعدور (ال1) https://www2h4xl8y7n5/diyar-al\n", "\n", "English: The train arrives at 7 PM.\n", "Arabic: بإنتاسيار موعلة 5 فهمد 3 البالً 4 وسَ� 6 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n", "\n", "English: I enjoy listening to music while working.\n", "Arabic: أحمد في النترقائة والعباول مسه, #al-Baqara https://tribulationworld.wordpress/\n", "‹�Euph\n", "\n", "English: The meeting has been scheduled for tomorrow.\n", "Arabic: أعد كتابة الجملين والورباهً مسی \"Jihad\". #jihadi\n", "’- The following is a list of the items that\n", "\n", "English: Can you recommend a good restaurant nearby?\n", "Arabic: أعد قائمة الصنيف والسبار. مهوتل: \"A great meal at the best price\". #jihadpics\n", "‌🊥\n", "\n", "English: I have finished my homework for the day.\n", "Arabic: كتبين بالمشاء العلاقة وهو مسرد \"Amar\" (5) 1/2\". #93529\n", "The following morning, I woke\n", "\n", "English: The city is known for its beautiful parks.\n", "Arabic: يمشكون إليها مراسعة البدال وستیک \"Husain\" = the mountain of mountains, which stands at top and meets with\n", "\n", "English: Do you prefer tea or coffee?\n", "Arabic: أعد قصير من الكلمة والتواعبا.\n", "سهِ: \"I like to go on walks and try new things.\" #tea pic 1 https\n", "\n" ] } ], "source": [ "\n", "import re\n", "import torch\n", "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n", "\n", "# Test sentences for translation (these are just examples)\n", "test_samples = [\n", " \"The cat is sitting on the window.\",\n", " \"I need to buy a new phone.\",\n", " \"She loves reading books during her free time.\",\n", " \"The train arrives at 7 PM.\",\n", " \"I enjoy listening to music while working.\",\n", " \"The meeting has been scheduled for tomorrow.\",\n", " \"Can you recommend a good restaurant nearby?\",\n", " \"I have finished my homework for the day.\",\n", " \"The city is known for its beautiful parks.\",\n", " \"Do you prefer tea or coffee?\"\n", "]\n", "\n", "\n", "# Add a special token for separator\n", "tokenizer.add_special_tokens({'additional_special_tokens': [\"<|sep|>\"]})\n", "model.resize_token_embeddings(len(tokenizer))\n", "\n", "# Function to translate sentences\n", "def translate_sentence(model, tokenizer, sentence):\n", " input_text = f\"{sentence} <|sep|>\"\n", " input_ids = tokenizer(input_text, return_tensors=\"pt\").input_ids.to(model.device)\n", " attention_mask = tokenizer(input_text, return_tensors=\"pt\").attention_mask.to(model.device)\n", "\n", " # Generate translation\n", " outputs = model.generate(\n", " input_ids,\n", " attention_mask=attention_mask,\n", " max_new_tokens=50,\n", " do_sample=True,\n", " temperature=0.7,\n", " top_p=0.9,\n", " top_k=50,\n", " repetition_penalty=3.0,\n", " early_stopping=True,\n", " pad_token_id=tokenizer.pad_token_id\n", " )\n", "\n", " translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", "\n", " # Clean up translated text (remove anything before punctuation marks)\n", " match = re.search(r'[.?!]', translated_text)\n", " if match:\n", " translated_text = translated_text[match.end():].strip()\n", "\n", " return translated_text\n", "\n", "# Translate test sentences\n", "print(\"\\nTest Sentence Translations:\")\n", "for sentence in test_samples:\n", " translation = translate_sentence(model, tokenizer, sentence)\n", " print(f\"English: {sentence}\")\n", " print(f\"Arabic: {translation}\\n\")\n" ] }, { "cell_type": "code", "source": [ "# Perplexity Calculation\n", "# Perplexity Calculation\n", "def calculate_perplexity(model, tokenizer, sentences):\n", " inputs = tokenizer(sentences, return_tensors=\"pt\", padding=True, truncation=True, max_length=128)\n", " input_ids = inputs['input_ids'].to(model.device)\n", " attention_mask = inputs['attention_mask'].to(model.device)\n", "\n", " with torch.no_grad():\n", " outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)\n", " loss = outputs.loss\n", " return torch.exp(loss).item()\n", "\n", "# CHRF Score Calculation\n", "def calculate_chrf_score(references, translations):\n", " # sacrebleu's chrf_score expects the references and translations as a list of strings.\n", " chrf = sacrebleu.corpus_chrf(references, translations)\n", " return chrf.score\n", "\n", "# BLEU Score Calculation\n", "def calculate_bleu_score(references, translations):\n", " references = [[ref.split()] for ref in references]\n", " translations = [trans.split() for trans in translations]\n", " return corpus_bleu(references, translations)\n", "\n", "# Evaluate translations\n", "translated_sentences = [translate_sentence(model, tokenizer, s) for s in en_sentences[:5]]\n", "perplexity = calculate_perplexity(model, tokenizer, en_sentences[:5])\n", "bleu_score = calculate_bleu_score(ar_sentences[:5], translated_sentences)\n", "chrf = calculate_chrf_score(ar_sentences[:5], translated_sentences)\n", "\n", "print(f\"\\nPerplexity: {perplexity}\")\n", "print(f\"BLEU Score: {bleu_score}\")\n", "print(f\"CHRF Score: {chrf}\")\n", "\n", "\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lDmYGA_PgSdW", "outputId": "1098d48f-db88-4756-81d3-902fdcf84edc" }, "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Perplexity: 2849.4482421875\n", "BLEU Score: 0\n", "CHRF Score: 16.0\n" ] } ] }, { "cell_type": "code", "source": [ "# Plotting Results\n", "def plot_results(perplexity, bleu_score, chrf):\n", " # Metrics for plotting\n", " metrics = ['Perplexity', 'BLEU Score', 'CHRF Score']\n", " values = [perplexity, bleu_score, chrf]\n", "\n", " plt.figure(figsize=(8, 6))\n", " plt.bar(metrics, values, color=['blue', 'green', 'orange'])\n", " plt.xlabel('Metrics')\n", " plt.ylabel('Scores')\n", " plt.title('Model Evaluation Metrics')\n", " plt.show()\n", "\n", "# Plot the results\n", "plot_results(perplexity, bleu_score, chrf)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 564 }, "id": "WxIoO2bbmh3g", "outputId": "99e44007-a372-45e1-b672-7344afecc9ef" }, "execution_count": 19, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "

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\n" }, "metadata": {} } ] } ] }