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markdown-it-py>=2.2.0->rich>=12.3.0->typer->huggingface-hub>=0.20.0->sentence-transformers) (0.1.2)\n", + "Downloading faiss_cpu-1.13.2-cp310-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (23.8 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.8/23.8 MB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: faiss-cpu\n", + "Successfully installed faiss-cpu-1.13.2\n" + ] + } + ], + "source": [ + "pip install sentence-transformers faiss-cpu numpy" + ] + }, + { + "cell_type": "code", + "source": [ + "pip install nltk" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "P1q0tTkZWYL6", + "outputId": "9f7fe29b-e951-4703-9226-9ef092fff4a4" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: nltk in /usr/local/lib/python3.12/dist-packages (3.9.1)\n", + "Requirement already satisfied: click in /usr/local/lib/python3.12/dist-packages (from nltk) (8.3.1)\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.12/dist-packages (from nltk) (1.5.3)\n", + "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.12/dist-packages (from nltk) (2025.11.3)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from nltk) (4.67.3)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import nltk\n", + "\n", + "nltk.download('punkt')\n", + "nltk.download('punkt_tab')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JZLP7kwlWbNQ", + "outputId": "1affcdd9-962a-465e-dd34-dcd970ccd83e" + }, + "execution_count": 3, + "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 punkt_tab to /root/nltk_data...\n", + "[nltk_data] Unzipping tokenizers/punkt_tab.zip.\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import json\n", + "\n", + "with open(\"moad_cleaned_spacy.json\", \"r\", encoding=\"utf-8\") as f:\n", + " data = json.load(f)\n", + "\n", + "texts = [item[\"Text\"] for item in data]" + ], + "metadata": { + "id": "ajo-Gpv-UOmZ" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def chunk_text(text, chunk_size=300, overlap=100):\n", + " chunks = []\n", + " start = 0\n", + "\n", + " while start < len(text):\n", + " end = start + chunk_size\n", + " chunk = text[start:end]\n", + " chunks.append(chunk)\n", + " start += chunk_size - overlap\n", + "\n", + " return chunks\n", + "\n", + "\n", + "all_chunks = []\n", + "\n", + "for text in texts:\n", + " chunks = chunk_text(text)\n", + " all_chunks.extend(chunks)\n", + "\n", + "print(\"Total chunks:\", len(all_chunks))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GdgGVv9AUOo0", + "outputId": "2d6da2a0-0a49-4bf6-f96a-9aed79da862d" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total chunks: 283\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# from nltk.tokenize import sent_tokenize\n", + "\n", + "# def chunk_by_sentences(text, max_words=120):\n", + "\n", + "# sentences = sent_tokenize(text)\n", + "\n", + "# chunks = []\n", + "# current_chunk = []\n", + "# word_count = 0\n", + "\n", + "# for sentence in sentences:\n", + "# words = sentence.split()\n", + "\n", + "# if word_count + len(words) <= max_words:\n", + "# current_chunk.append(sentence)\n", + "# word_count += len(words)\n", + "\n", + "# else:\n", + "# chunks.append(\" \".join(current_chunk))\n", + "# current_chunk = [sentence]\n", + "# word_count = len(words)\n", + "\n", + "# if current_chunk:\n", + "# chunks.append(\" \".join(current_chunk))\n", + "\n", + "# return chunks\n", + "\n", + "\n", + "# all_chunks = []\n", + "\n", + "# for text in texts:\n", + "# all_chunks.extend(chunk_by_sentences(text))\n", + "\n", + "# print(\"Total chunks:\", len(all_chunks))" + ], + "metadata": { + "id": "nfwBQvMaWQ9j" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sentence_transformers import SentenceTransformer\n", + "\n", + "model = SentenceTransformer(\"BAAI/bge-m3\")\n", + "\n", + "embeddings = model.encode(\n", + " all_chunks,\n", + " show_progress_bar=True\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 624, + "referenced_widgets": [ + "46b81dee680a4c9ba935efbed57ef26d", + "30f275e87cc64233968b3727d31c639b", + "935a63f7b2af4bf6834ad79e13958aa6", + "b1dd8f45a1554ee6ae5ecb04fa2d1570", + 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"/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: 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": [ + "modules.json: 0%| | 0.00/349 [00:00