{"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":[{"sourceId":121150,"databundleVersionId":14976537,"sourceType":"competition"}],"dockerImageVersionId":31234,"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":"2025-12-21T09:48:59.433674Z","iopub.execute_input":"2025-12-21T09:48:59.434076Z","iopub.status.idle":"2025-12-21T09:49:01.278219Z","shell.execute_reply.started":"2025-12-21T09:48:59.434027Z","shell.execute_reply":"2025-12-21T09:49:01.276005Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\n\n# Load test data\ntest_df = pd.read_csv(\n    \"/kaggle/input/deep-past-initiative-machine-translation/test.csv\"\n)\n\nprint(\"Columns in test.csv:\", test_df.columns.tolist())\n\n# Minimal valid English translation (single sentence)\ndef baseline_translate(translit: str) -> str:\n    return \"This is a commercial document written by an Assyrian merchant.\"\n\n# Build submission (IMPORTANT column names)\nsubmission = pd.DataFrame({\n    \"id\": test_df[\"id\"],\n    \"translation\": test_df[\"transliteration\"].apply(baseline_translate)\n})\n\n# REQUIRED filename\nsubmission.to_csv(\"submission.csv\", index=False)\n\nprint(\"✅ submission.csv created successfully\")\nsubmission.head()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-21T09:50:49.558145Z","iopub.execute_input":"2025-12-21T09:50:49.558550Z","iopub.status.idle":"2025-12-21T09:50:49.601847Z","shell.execute_reply.started":"2025-12-21T09:50:49.558520Z","shell.execute_reply":"2025-12-21T09:50:49.600528Z"}},"outputs":[],"execution_count":null}]}