diff --git "a/Topic_classifiication/TopicModel.ipynb" "b/Topic_classifiication/TopicModel.ipynb"
deleted file mode 100644--- "a/Topic_classifiication/TopicModel.ipynb"
+++ /dev/null
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- "## Environment Setup, Drive Mount, Data Loading & Cleaning\n",
- "- Install required libraries\n",
- "- Mount Google Drive for persistent saving\n",
- "- Load the `arbml/ashaar` dataset\n",
- "- Drop \"قصيدة 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"
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- "\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"
- ]
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- "output_type": "display_data",
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- "README.md: 0.00B [00:00, ?B/s]"
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- "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"
- ]
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- "dataset_infos.json: 0.00B [00:00, ?B/s]"
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- "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",
- "print(f\"\\nTokenized datasets + tokenizer saved to Drive ✓\")"
- ],
- "metadata": {
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- "height": 893,
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