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عامه\", \"قصيدة قصيره\", and tiny classes\n", + "- Drop poems with < 3 verses\n", + "- Apply Arabic text normalization (strip diacritics, normalize alef, remove tatweel)\n", + "- Show final class distribution" + ], + "metadata": { + "id": "ZQBUru6B_VfM" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Mv9VrAnD_UsV", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 839, + "referenced_widgets": [ + "8e51a29e378a4efab7435fe80c98942d", + "a37b5387243147388d3e65056e57fc62", + "311ac9940b034c778b1d072013c7148c", + "78cba477588d4feb9aaa279c0a5251cd", + "3a84b80423d949da8182a572245d31c9", + "9c97b4bf37af4934818d4bee449463f2", + "7b44100ea1914192a8c5001d20a36903", + "660bd3f38b634484b2d846b263d3e207", + "477944f00c6f443382424521c6e52ba9", + "fe5628ec6c79440aaecf2cc057cb368f", + "0cfbb83f10ce4f288cdc8dcbcf461161", + "b27007ee5f2347d6a05cfab0765418ae", + "22b1a41b168740a4a9d8dec287ddcd0f", + "43c7736a25b84f21b54fadb104a73385", + 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"a86a0465b32641cba03dd8c769968946", + "f7653ba4ac684619bbd67d847f7d656f", + "6cad91173884431a9d25ebd9d62704d7", + "f8b403ccec6f44e7b39fe3f994289e33", + "c1a5b36c5a36425ca1cc3fd51e6554d6", + "1f398286932f422f8a029d0a58b4dca4", + "206d8fdec93d4b4095e4a24b49bf61c0", + "655bc4ccc71d485dbc71d1be7f9993b0", + "058fcfe7ae604924864f487622baf413", + "03991c583656430795714a28bc36bf2d", + "eadf75be53a64feba4b29d0994366479", + "abd9974da7cd4be3ae3a9cdd233129fe", + "5e184ea876ff4d86833af2d728fbe5f2", + "bef3b033e7dc4479847d5892c6f2a39c", + "58826c98d96b416c87995aafccd7e15a" + ] + }, + "outputId": "c6d45b24-ad06-45ba-f23a-9985996a1efd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hMounted at /content/drive\n", + "Save directory: /content/drive/MyDrive/arabic_poetry_topic_model\n", + "\n", + "Loading dataset...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:93: 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": [ + "README.md: 0.00B [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "8e51a29e378a4efab7435fe80c98942d" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n", + "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "dataset_infos.json: 0.00B [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "b27007ee5f2347d6a05cfab0765418ae" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "data/train-00000-of-00002.parquet: 0%| | 0.00/126M [00:00= 2:\n", + " lines = []\n", + " for i in range(0, len(items) - 1, 2):\n", + " lines.append(items[i] + \" \" + items[i + 1])\n", + " if len(items) % 2 == 1:\n", + " lines.append(items[-1])\n", + " return \"\\n\".join(lines)\n", + " return \"\\n\".join(items)\n", + " return \"\"\n", + "\n", + "df[\"poem_text_raw\"] = df[\"poem verses\"].apply(build_poem_text)\n", + "df[\"num_verses\"] = df[\"poem verses\"].apply(\n", + " lambda x: len(x) if isinstance(x, (list, np.ndarray)) else 0\n", + ")\n", + "\n", + "# Arabic normalization\n", + "ARABIC_DIACRITICS = re.compile(r'[\\u0617-\\u061A\\u064B-\\u0652\\u0670\\u06D6-\\u06ED]')\n", + "\n", + "def normalize_arabic(text):\n", + " if not text:\n", + " return \"\"\n", + " text = str(text)\n", + " text = text.replace(\"ـ\", \"\") # tatweel\n", + " text = ARABIC_DIACRITICS.sub(\"\", text) # diacritics\n", + " text = re.sub(r'[أإآٱ]', 'ا', text) # alef variants\n", + " text = text.replace(\"ة\", \"ه\") # taa marbuta\n", + " text = re.sub(r'[^\\w\\s\\u0600-\\u06FF]', ' ', text) # non-Arabic punctuation\n", + " text = re.sub(r'\\s+', ' ', text).strip() # whitespace\n", + " return text\n", + "\n", + "df[\"poem_text\"] = df[\"poem_text_raw\"].apply(normalize_arabic)\n", + "df[\"word_count\"] = df[\"poem_text\"].apply(lambda x: len(x.split()))\n", + "\n", + "# Filtering\n", + "# 1) Drop \"قصيدة عامه\" and \"قصيدة قصيره\"\n", + "exclude_themes = [\"قصيدة عامه\", \"قصيدة قصيره\"]\n", + "before = len(df)\n", + "df = df[~df[\"poem theme\"].isin(exclude_themes)].reset_index(drop=True)\n", + "print(f\"After dropping عامه + قصيره: {len(df):,} (removed {before - len(df):,})\")\n", + "\n", + "# 2) Drop very short poems (< 3 verses = < 6 items typically)\n", + "before = len(df)\n", + "df = df[df[\"num_verses\"] >= 6].reset_index(drop=True) # 6 items = 3 full lines\n", + "print(f\"After dropping < 3 lines: {len(df):,} (removed {before - len(df):,})\")\n", + "\n", + "# 3) Drop tiny classes (< 50 poems)\n", + "theme_counts = df[\"poem theme\"].value_counts()\n", + "tiny_themes = theme_counts[theme_counts < 50].index.tolist()\n", + "if tiny_themes:\n", + " before = len(df)\n", + " df = df[~df[\"poem theme\"].isin(tiny_themes)].reset_index(drop=True)\n", + " print(f\"Dropped tiny classes {tiny_themes}: removed {before - len(df):,}\")\n", + "\n", + "# Final distribution\n", + "print(f\"\\n{'='*60}\")\n", + "print(f\"FINAL DATASET: {len(df):,} poems | {df['poem theme'].nunique()} classes\")\n", + "print(f\"Unique poets: {df['poet name'].nunique()}\")\n", + "print(f\"{'='*60}\")\n", + "final_dist = df[\"poem theme\"].value_counts()\n", + "for theme, count in final_dist.items():\n", + " pct = count / len(df) * 100\n", + " print(f\" {theme:25s} → {count:>5,} ({pct:5.1f}%)\")\n", + "\n", + "# Save cleaned data\n", + "df.to_pickle(os.path.join(SAVE_DIR, \"df_cleaned.pkl\"))\n", + "print(f\"\\nCleaned data saved to Drive ✓\")" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Poet-Aware Train/Val/Test Split + Class Weights\n", + "- Split poets (not poems) into 80/10/10\n", + "- Assign poems to splits based on their poet\n", + "- Verify no poet leaks across splits\n", + "- Compute class weights for imbalanced loss\n", + "- Save split info to Drive" + ], + "metadata": { + "id": "Kpa3G2kfA47S" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import torch\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.utils.class_weight import compute_class_weight\n", + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "SEED = 42\n", + "\n", + "# Encode labels\n", + "le = LabelEncoder()\n", + "df[\"label\"] = le.fit_transform(df[\"poem theme\"])\n", + "label_names = list(le.classes_)\n", + "num_labels = len(label_names)\n", + "id2label = {i: label_names[i] for i in range(num_labels)}\n", + "label2id = {v: k for k, v in id2label.items()}\n", + "\n", + "print(f\"Number of classes: {num_labels}\")\n", + "for i, name in id2label.items():\n", + " print(f\" {i} → {name}\")\n", + "\n", + "# Poet-level split\n", + "poets = sorted(df[\"poet name\"].dropna().unique())\n", + "print(f\"\\nTotal unique poets: {len(poets)}\")\n", + "\n", + "train_poets, temp_poets = train_test_split(poets, test_size=0.2, random_state=SEED)\n", + "val_poets, test_poets = train_test_split(temp_poets, test_size=0.5, random_state=SEED)\n", + "\n", + "print(f\"Train poets: {len(train_poets)} | Val poets: {len(val_poets)} | Test poets: {len(test_poets)}\")\n", + "\n", + "# Assign splits\n", + "def assign_split(poet_name):\n", + " if poet_name in set(train_poets):\n", + " return \"train\"\n", + " elif poet_name in set(val_poets):\n", + " return \"val\"\n", + " else:\n", + " return \"test\"\n", + "\n", + "df[\"split\"] = df[\"poet name\"].apply(assign_split)\n", + "\n", + "# Verify no leakage\n", + "train_set = set(df[df[\"split\"]==\"train\"][\"poet name\"])\n", + "val_set = set(df[df[\"split\"]==\"val\"][\"poet name\"])\n", + "test_set = set(df[df[\"split\"]==\"test\"][\"poet name\"])\n", + "assert len(train_set & val_set) == 0, \"LEAK: train-val overlap!\"\n", + "assert len(train_set & test_set) == 0, \"LEAK: train-test overlap!\"\n", + "assert len(val_set & test_set) == 0, \"LEAK: val-test overlap!\"\n", + "print(\"✓ No poet leakage across splits\")\n", + "\n", + "# Split sizes\n", + "print(f\"\\nSplit sizes:\")\n", + "for sp in [\"train\", \"val\", \"test\"]:\n", + " n = len(df[df[\"split\"]==sp])\n", + " print(f\" {sp:6s}: {n:>6,} poems\")\n", + "\n", + "# Class distribution per split\n", + "print(f\"\\nClass distribution per split:\")\n", + "ct = pd.crosstab(df[\"poem theme\"], df[\"split\"])\n", + "ct = ct[[\"train\", \"val\", \"test\"]]\n", + "print(ct.to_string())\n", + "\n", + "# Compute class weights\n", + "train_labels = df[df[\"split\"]==\"train\"][\"label\"].values\n", + "class_weights = compute_class_weight(\"balanced\", classes=np.arange(num_labels), y=train_labels)\n", + "class_weights_tensor = torch.tensor(class_weights, dtype=torch.float32)\n", + "\n", + "print(f\"\\nClass weights (balanced):\")\n", + "for i in range(num_labels):\n", + " print(f\" {id2label[i]:25s} → {class_weights[i]:.3f}\")\n", + "\n", + "# Save to Drive\n", + "import pickle\n", + "\n", + "split_info = {\n", + " \"label_encoder\": le,\n", + " \"id2label\": id2label,\n", + " \"label2id\": label2id,\n", + " \"num_labels\": num_labels,\n", + " \"train_poets\": train_poets,\n", + " \"val_poets\": val_poets,\n", + " \"test_poets\": test_poets,\n", + " \"class_weights\": class_weights,\n", + "}\n", + "\n", + "with open(os.path.join(SAVE_DIR, \"split_info.pkl\"), \"wb\") as f:\n", + " pickle.dump(split_info, f)\n", + "\n", + "df.to_pickle(os.path.join(SAVE_DIR, \"df_with_splits.pkl\"))\n", + "print(f\"\\nSplit info + data saved to Drive ✓\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "47rblPSBA8AE", + "outputId": "caa7574a-fb19-45ee-fd80-38758d88b857" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Number of classes: 12\n", + " 0 → قصيدة حزينه\n", + " 1 → قصيدة دينية\n", + " 2 → قصيدة ذم\n", + " 3 → قصيدة رثاء\n", + " 4 → قصيدة رومنسيه\n", + " 5 → قصيدة شوق\n", + " 6 → قصيدة عتاب\n", + " 7 → قصيدة غزل\n", + " 8 → قصيدة فراق\n", + " 9 → قصيدة مدح\n", + " 10 → قصيدة هجاء\n", + " 11 → قصيدة وطنيه\n", + "\n", + "Total unique poets: 331\n", + "Train poets: 264 | Val poets: 33 | Test poets: 34\n", + "✓ No poet leakage across splits\n", + "\n", + "Split sizes:\n", + " train : 14,555 poems\n", + " val : 959 poems\n", + " test : 2,856 poems\n", + "\n", + "Class distribution per split:\n", + "split train val test\n", + "poem theme \n", + "قصيدة حزينه 1607 113 380\n", + "قصيدة دينية 913 68 96\n", + "قصيدة ذم 244 7 45\n", + "قصيدة رثاء 620 51 142\n", + "قصيدة رومنسيه 2877 257 599\n", + "قصيدة شوق 636 44 123\n", + "قصيدة عتاب 1287 98 437\n", + "قصيدة غزل 929 52 171\n", + "قصيدة فراق 355 21 116\n", + "قصيدة مدح 3835 193 467\n", + "قصيدة هجاء 1030 39 212\n", + "قصيدة وطنيه 222 16 68\n", + "\n", + "Class weights (balanced):\n", + " قصيدة حزينه → 0.755\n", + " قصيدة دينية → 1.328\n", + " قصيدة ذم → 4.971\n", + " قصيدة رثاء → 1.956\n", + " قصيدة رومنسيه → 0.422\n", + " قصيدة شوق → 1.907\n", + " قصيدة عتاب → 0.942\n", + " قصيدة غزل → 1.306\n", + " قصيدة فراق → 3.417\n", + " قصيدة مدح → 0.316\n", + " قصيدة هجاء → 1.178\n", + " قصيدة وطنيه → 5.464\n", + "\n", + "Split info + data saved to Drive ✓\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Tokenize with CAMeLBERT-CA (Classical Arabic) + Build HF Datasets\n", + "- Load `CAMeL-Lab/bert-base-arabic-camelbert-ca` tokenizer\n", + "- Tokenize normalized poem text with max_length=512\n", + "- Build HuggingFace DatasetDict for train/val/test\n", + "- Audit token length coverage at 512" + ], + "metadata": { + "id": "-DMDUcHnBI8W" + } + }, + { + "cell_type": "code", + "source": [ + "from datasets import Dataset, DatasetDict\n", + "from transformers import AutoTokenizer\n", + "\n", + "MODEL_NAME = \"CAMeL-Lab/bert-base-arabic-camelbert-ca\"\n", + "MAX_LENGTH = 512\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n", + "print(f\"Tokenizer: {MODEL_NAME}\")\n", + "print(f\"Max length: {MAX_LENGTH}\")\n", + "print(f\"Vocab size: {tokenizer.vocab_size}\")\n", + "\n", + "# Quick token length audit (before truncation)\n", + "print(\"\\nToken length audit (no truncation)...\")\n", + "sample = df.sample(min(3000, len(df)), random_state=SEED)\n", + "lengths = sample[\"poem_text\"].apply(lambda x: len(tokenizer.encode(x, add_special_tokens=True)))\n", + "print(f\" Mean: {lengths.mean():.0f}\")\n", + "print(f\" Median: {lengths.median():.0f}\")\n", + "print(f\" P90: {lengths.quantile(0.9):.0f}\")\n", + "print(f\" P95: {lengths.quantile(0.95):.0f}\")\n", + "print(f\" Max: {lengths.max()}\")\n", + "pct_within = (lengths <= MAX_LENGTH).mean() * 100\n", + "print(f\" % fitting in {MAX_LENGTH} tokens: {pct_within:.1f}%\")\n", + "\n", + "# Build HF datasets\n", + "def make_hf_dataset(split_name):\n", + " split_df = df[df[\"split\"] == split_name].copy()\n", + " return Dataset.from_dict({\n", + " \"text\": split_df[\"poem_text\"].tolist(),\n", + " \"labels\": split_df[\"label\"].tolist(),\n", + " })\n", + "\n", + "raw_datasets = DatasetDict({\n", + " \"train\": make_hf_dataset(\"train\"),\n", + " \"val\": make_hf_dataset(\"val\"),\n", + " \"test\": make_hf_dataset(\"test\"),\n", + "})\n", + "\n", + "print(f\"\\nRaw datasets:\")\n", + "for sp in [\"train\", \"val\", \"test\"]:\n", + " print(f\" {sp}: {len(raw_datasets[sp]):,}\")\n", + "\n", + "# Tokenize\n", + "def tokenize_fn(batch):\n", + " return tokenizer(\n", + " batch[\"text\"],\n", + " truncation=True,\n", + " max_length=MAX_LENGTH,\n", + " padding=False, # dynamic padding via data collator\n", + " )\n", + "\n", + "tokenized_datasets = raw_datasets.map(\n", + " tokenize_fn,\n", + " batched=True,\n", + " batch_size=1000,\n", + " remove_columns=[\"text\"],\n", + " desc=\"Tokenizing\",\n", + ")\n", + "\n", + "print(f\"\\nTokenized datasets:\")\n", + "for sp in [\"train\", \"val\", \"test\"]:\n", + " print(f\" {sp}: {tokenized_datasets[sp]}\")\n", + "\n", + "# Save tokenized datasets to Drive\n", + "tokenized_datasets.save_to_disk(os.path.join(SAVE_DIR, \"tokenized_datasets\"))\n", + "tokenizer.save_pretrained(os.path.join(SAVE_DIR, \"tokenizer\"))\n", + 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EpochTraining LossValidation LossAccuracyMacro F1Weighted F1
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EpochTraining LossValidation LossAccuracyMacro F1Weighted F1
13.4297961.6576680.4202290.3681040.392224
23.1604751.6182160.4671530.4078670.473069
32.4092671.7266250.4921790.4222490.494300
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60.9803472.2570800.4932220.4161910.495103

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EpochTraining LossValidation LossAccuracyMacro F1Weighted F1
12.5106051.2377890.5537020.5082250.554203
22.3437721.1784220.5870700.5325800.594504
31.5812591.2890640.6027110.5460090.604731
41.2607111.4923680.5922840.5250180.596571
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60.5438682.0803990.6006260.5168290.601528

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\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " eval_loss: 1.2891\n", + " eval_accuracy: 0.6027\n", + " eval_macro_f1: 0.5460\n", + " eval_weighted_f1: 0.6047\n", + " eval_runtime: 1.6887\n", + " eval_samples_per_second: 567.8790\n", + " eval_steps_per_second: 17.7650\n", + " epoch: 6.0000\n", + "\n", + "📊 Test metrics:\n", + " eval_loss: 1.3896\n", + " eval_accuracy: 0.5609\n", + " eval_macro_f1: 0.5026\n", + " eval_weighted_f1: 0.5655\n", + " eval_runtime: 4.8792\n", + " eval_samples_per_second: 585.3400\n", + " eval_steps_per_second: 18.4460\n", + " epoch: 6.0000\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Writing model shards: 0%| | 0/1 [00:00