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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", "64b91e1b52014274ab17ef1f14c41091", "30669d5d399c4489a5e6b4300dad9530", "8334a5b409be4ecfb3e84565ac0fd67f", "45b12d7b26c34047a7eac4ad613df6cb", "843420251fd344d283da76125358ce3c", "2b645e69accc42e5a4ac03ad4f23ce25", "57fa46b58d004025a97731830277e18b", "e99ff50da41243c69587b6ccf11b6165", "e42d35f22a3b4ce7bf778c2bb96dd663", "c1abd3c876804a449172503ac582b006", "fd9fd349836045d89c418786506e926d", "a1a46995b6ee4654ae415c2c5663c019", "24aa0facf5044927b2274a3b0fca1676", "71e09516bf0845f89f1ef1c7baae6673", 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"b1039ca388314a9281297f39718c196b" ] }, "id": "W1jqFKkVUOrT", "outputId": "25d30457-b775-4fdb-da5a-0fc562124a8b" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/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