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{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":10019907,"sourceType":"datasetVersion","datasetId":6169792}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install rank_bm25","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T12:55:34.945215Z","iopub.execute_input":"2024-11-27T12:55:34.945467Z","iopub.status.idle":"2024-11-27T12:55:44.655417Z","shell.execute_reply.started":"2024-11-27T12:55:34.945442Z","shell.execute_reply":"2024-11-27T12:55:44.654556Z"}},"outputs":[{"name":"stdout","text":"Collecting rank_bm25\n Downloading rank_bm25-0.2.2-py3-none-any.whl.metadata (3.2 kB)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from rank_bm25) (1.26.4)\nDownloading rank_bm25-0.2.2-py3-none-any.whl (8.6 kB)\nInstalling collected packages: rank_bm25\nSuccessfully installed rank_bm25-0.2.2\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"!pip install sentence_transformers","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T12:55:44.657566Z","iopub.execute_input":"2024-11-27T12:55:44.657961Z","iopub.status.idle":"2024-11-27T12:55:53.264757Z","shell.execute_reply.started":"2024-11-27T12:55:44.657921Z","shell.execute_reply":"2024-11-27T12:55:53.263881Z"}},"outputs":[{"name":"stdout","text":"Collecting sentence_transformers\n Downloading sentence_transformers-3.3.1-py3-none-any.whl.metadata (10 kB)\nRequirement already satisfied: transformers<5.0.0,>=4.41.0 in /opt/conda/lib/python3.10/site-packages (from sentence_transformers) (4.45.1)\nRequirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from sentence_transformers) (4.66.4)\nRequirement already satisfied: torch>=1.11.0 in /opt/conda/lib/python3.10/site-packages (from sentence_transformers) (2.4.0)\nRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (from sentence_transformers) (1.2.2)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (from sentence_transformers) (1.14.1)\nRequirement already satisfied: huggingface-hub>=0.20.0 in /opt/conda/lib/python3.10/site-packages (from sentence_transformers) (0.25.1)\nRequirement already satisfied: Pillow in /opt/conda/lib/python3.10/site-packages (from sentence_transformers) (10.3.0)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.20.0->sentence_transformers) (3.15.1)\nRequirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.20.0->sentence_transformers) (2024.6.1)\nRequirement already satisfied: packaging>=20.9 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.20.0->sentence_transformers) (21.3)\nRequirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.20.0->sentence_transformers) (6.0.2)\nRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.20.0->sentence_transformers) (2.32.3)\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.20.0->sentence_transformers) (4.12.2)\nRequirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.11.0->sentence_transformers) (1.13.3)\nRequirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.11.0->sentence_transformers) (3.3)\nRequirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.11.0->sentence_transformers) (3.1.4)\nRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from transformers<5.0.0,>=4.41.0->sentence_transformers) (1.26.4)\nRequirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers<5.0.0,>=4.41.0->sentence_transformers) (2024.5.15)\nRequirement already satisfied: safetensors>=0.4.1 in /opt/conda/lib/python3.10/site-packages (from transformers<5.0.0,>=4.41.0->sentence_transformers) (0.4.5)\nRequirement already satisfied: tokenizers<0.21,>=0.20 in /opt/conda/lib/python3.10/site-packages (from transformers<5.0.0,>=4.41.0->sentence_transformers) (0.20.0)\nRequirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->sentence_transformers) (1.4.2)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->sentence_transformers) (3.5.0)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.9->huggingface-hub>=0.20.0->sentence_transformers) (3.1.2)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.11.0->sentence_transformers) (2.1.5)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.20.0->sentence_transformers) (3.3.2)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.20.0->sentence_transformers) (3.7)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.20.0->sentence_transformers) (1.26.18)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.20.0->sentence_transformers) (2024.8.30)\nRequirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.11.0->sentence_transformers) (1.3.0)\nDownloading sentence_transformers-3.3.1-py3-none-any.whl (268 kB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hInstalling collected packages: sentence_transformers\nSuccessfully installed sentence_transformers-3.3.1\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"from rank_bm25 import BM25Okapi\nimport numpy as np\nfrom tqdm import tqdm\nimport os\nimport pickle\nimport torch\nimport glob\nimport json\n\nfrom sentence_transformers import SentenceTransformer","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T12:55:53.265931Z","iopub.execute_input":"2024-11-27T12:55:53.266227Z","iopub.status.idle":"2024-11-27T12:56:10.674019Z","shell.execute_reply.started":"2024-11-27T12:55:53.266199Z","shell.execute_reply":"2024-11-27T12:56:10.673097Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"def tokenize_doc_to_str(doc: dict) -> str:\n tokenized_doc = []\n for key, value in doc.items():\n tokenized_key = key.lower().replace(\"_\", \" \")\n tokenized_doc.append(tokenized_key)\n tokenized_doc.append(':')\n if isinstance(value, str):\n tokenized_doc.extend(value)\n else:\n tokenized_doc.extend(str(value))\n return ' '.join(tokenized_doc)\n\n# Take all json files with names that end '_processed' \nbase_path = \"/kaggle/input/jokerbot-rag/Kaggle_rag_data\"\nsbert_embeddings_path = \"/kaggle/working/sbert_embeddings.pt\"\n\ndocs = []\nfor path in glob.glob(f\"{base_path}/*_processed.json\"):\n with open(path, 'r') as f:\n docs.extend(json.load(f))\n\n# Initialize SentenceTransformer and ensure it uses GPU\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nsbert = SentenceTransformer('sentence-transformers/all-distilroberta-v1', device=device)\n\nstr_docs = [tokenize_doc_to_str(doc) for doc in docs]\n\nsbert_embeddings = sbert.encode(str_docs, show_progress_bar=True, convert_to_tensor=True, batch_size=256)\nsbert_embeddings = sbert_embeddings.cpu() # Move to CPU before saving\n\ntorch.save(sbert_embeddings, sbert_embeddings_path)","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-11-26T16:28:45.856755Z","iopub.execute_input":"2024-11-26T16:28:45.857664Z","iopub.status.idle":"2024-11-26T17:09:55.260372Z","shell.execute_reply.started":"2024-11-26T16:28:45.857629Z","shell.execute_reply":"2024-11-26T17:09:55.259570Z"},"scrolled":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"modules.json: 0%| | 0.00/349 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0b52bbf9949f4976b189a192786846fa"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config_sentence_transformers.json: 0%| | 0.00/116 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f0a9da239f0c47bca86164d89798cfd0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"README.md: 0%| | 0.00/10.3k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a683cef50dd74dcaa67d52f1d97a9dde"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"sentence_bert_config.json: 0%| | 0.00/53.0 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"33356070ca5d454abbea04578494327e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json: 0%| | 0.00/653 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9508e6d0971b41988657462d62259b97"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors: 0%| | 0.00/328M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b22d0be96164490298dafee30d56761d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json: 0%| | 0.00/333 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"130ba66811e5449190b14c4f14a619f0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vocab.json: 0%| | 0.00/798k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ad07149ecc904262aba091597524528b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4e57fdf247e94acf9a433cf1a9b33c30"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json: 0%| | 0.00/1.36M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0b2b93c2ad274dc196b99cbd8c4de301"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json: 0%| | 0.00/239 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"367d7b6f8c9a4862abe963aafcfede94"}},"metadata":{}},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1617: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be deprecated in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"1_Pooling/config.json: 0%| | 0.00/190 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"37f7c0897b0b429286a1e6d2ccff1d30"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Batches: 0%| | 0/2938 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b1a38d2bde154b42b90028ccbee6716c"}},"metadata":{}}],"execution_count":6},{"cell_type":"code","source":"# Define the path and split size\nsbert_embeddings = torch.load(\"/kaggle/working/sbert_embeddings.pt\")\n\noutput_dir = \"/kaggle/working/embeddings_parts\"\nos.makedirs(output_dir, exist_ok=True)\nsplit_size = 1000 # Number of rows per split\nembeddings_size = sbert_embeddings.size(0)\n# print(sbert_embeddings[0:5].shape)\n\n# Split and save the tensor\nfor i in range(0, embeddings_size, split_size):\n end_idx = min(i + split_size, embeddings_size)\n part = sbert_embeddings[i:end_idx].clone()\n torch.save(part, os.path.join(output_dir, f\"embeddings_part_{i//split_size}.pt\"))\n\nprint(f\"Saved all parts to {output_dir}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T12:56:35.052049Z","iopub.execute_input":"2024-11-27T12:56:35.052424Z","iopub.status.idle":"2024-11-27T12:56:39.294517Z","shell.execute_reply.started":"2024-11-27T12:56:35.052389Z","shell.execute_reply":"2024-11-27T12:56:39.293551Z"}},"outputs":[{"name":"stderr","text":"/tmp/ipykernel_30/1355149148.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n sbert_embeddings = torch.load(\"/kaggle/working/sbert_embeddings.pt\")\n","output_type":"stream"},{"name":"stdout","text":"Saved all parts to /kaggle/working/embeddings_parts\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"# Load and combine\nloaded_parts = []\nfor file in sorted(os.listdir(output_dir)):\n if file.startswith(\"embeddings_part_\") and file.endswith(\".pt\"):\n part_path = os.path.join(output_dir, file)\n loaded_parts.append(torch.load(part_path))\n\nreconstructed_embeddings = torch.cat(loaded_parts, dim=0)\n\n# Verify the shape\nprint(\"Original shape:\", sbert_embeddings.shape)\nprint(\"Reconstructed shape:\", reconstructed_embeddings.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T12:57:19.130200Z","iopub.execute_input":"2024-11-27T12:57:19.130602Z","iopub.status.idle":"2024-11-27T12:57:21.832589Z","shell.execute_reply.started":"2024-11-27T12:57:19.130559Z","shell.execute_reply":"2024-11-27T12:57:21.831663Z"}},"outputs":[{"name":"stderr","text":"/tmp/ipykernel_30/672009738.py:6: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n loaded_parts.append(torch.load(part_path))\n","output_type":"stream"},{"name":"stdout","text":"Original shape: torch.Size([752114, 768])\nReconstructed shape: torch.Size([752114, 768])\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"!rm -rf /kaggle/working/embeddings_parts/*","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T12:56:16.699750Z","iopub.execute_input":"2024-11-27T12:56:16.700088Z","iopub.status.idle":"2024-11-27T12:56:18.335530Z","shell.execute_reply.started":"2024-11-27T12:56:16.700059Z","shell.execute_reply":"2024-11-27T12:56:18.334335Z"}},"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n pid, fd = os.forkpty()\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"!rm -rf /kaggle/working/embedding_parts.zip","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T12:56:18.589627Z","iopub.execute_input":"2024-11-27T12:56:18.590533Z","iopub.status.idle":"2024-11-27T12:56:20.052690Z","shell.execute_reply.started":"2024-11-27T12:56:18.590497Z","shell.execute_reply":"2024-11-27T12:56:20.051459Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"!zip -r embedding_parts.zip /kaggle/working/embeddings_parts","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-27T12:57:31.305877Z","iopub.execute_input":"2024-11-27T12:57:31.306220Z","iopub.status.idle":"2024-11-27T12:59:28.994129Z","shell.execute_reply.started":"2024-11-27T12:57:31.306190Z","shell.execute_reply":"2024-11-27T12:59:28.992959Z"},"scrolled":true},"outputs":[{"name":"stdout","text":" adding: kaggle/working/embeddings_parts/ (stored 0%)\n adding: 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