{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceType":"competition","sourceId":121150,"databundleVersionId":15745837}],"dockerImageVersionId":31286,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-02-23T09:00:57.118334Z","iopub.execute_input":"2026-02-23T09:00:57.119189Z","iopub.status.idle":"2026-02-23T09:00:57.133465Z","shell.execute_reply.started":"2026-02-23T09:00:57.119133Z","shell.execute_reply":"2026-02-23T09:00:57.131538Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"🏺 Deep Past Challenge — ONE-BLOCK MINIMAL CODE","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport re\n\n# load test data\ntest = pd.read_csv(\"/kaggle/input/deep-past-initiative-machine-translation/test.csv\")\n\n# ultra-simple cleaning\ndef clean(x):\n    x = re.sub(r\"\\[.*?\\]|<.*?>|[!?/:]\", \"\", x)\n    return x.strip()\n\n# VERY SIMPLE BASELINE TRANSLATION\n# (rule-based fallback, no ML, but valid submission)\ndef baseline_translate(x):\n    return \"Translation unavailable.\"\n\ntest[\"translation\"] = test[\"transliteration\"].apply(baseline_translate)\n\n# save submission\ntest[[\"id\", \"translation\"]].to_csv(\"submission.csv\", index=False)\n\nprint(\"submission.csv ready (internet-free)\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-23T09:00:57.256986Z","iopub.execute_input":"2026-02-23T09:00:57.257319Z","iopub.status.idle":"2026-02-23T09:00:57.283727Z","shell.execute_reply.started":"2026-02-23T09:00:57.257289Z","shell.execute_reply":"2026-02-23T09:00:57.282118Z"}},"outputs":[],"execution_count":null}]}