{"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":106809,"databundleVersionId":13056355,"sourceType":"competition"}],"dockerImageVersionId":31234,"isInternetEnabled":true,"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":"2025-12-21T09:55:53.198858Z","iopub.execute_input":"2025-12-21T09:55:53.199103Z","iopub.status.idle":"2025-12-21T09:55:55.086992Z","shell.execute_reply.started":"2025-12-21T09:55:53.199076Z","shell.execute_reply":"2025-12-21T09:55:55.085724Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\n\n# ------------------------------------------------------------\n# Brain-to-Text '25 requires EXACTLY 1450 test sentences\n# IDs must be 0 to 1449 in chronological order\n# ------------------------------------------------------------\n\nNUM_TEST_SENTENCES = 1450\n\n# Dummy decoded sentence\n# Rules:\n# - lowercase only\n# - no punctuation\n# - words separated by spaces\nDUMMY_SENTENCE = \"i am trying to say something\"\n\n# Create submission DataFrame\nsubmission = pd.DataFrame({\n    \"id\": range(NUM_TEST_SENTENCES),\n    \"text\": [DUMMY_SENTENCE] * NUM_TEST_SENTENCES\n})\n\n# Save required file\nsubmission.to_csv(\"submission.csv\", index=False)\n\nprint(\"✅ submission.csv created successfully\")\nsubmission.head()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-21T09:56:31.632721Z","iopub.execute_input":"2025-12-21T09:56:31.633094Z","iopub.status.idle":"2025-12-21T09:56:31.689192Z","shell.execute_reply.started":"2025-12-21T09:56:31.633060Z","shell.execute_reply":"2025-12-21T09:56:31.688280Z"}},"outputs":[],"execution_count":null}]}