{"metadata":{"kernelspec":{"display_name":"bs","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.12.12"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceType":"competition","sourceId":118765,"databundleVersionId":16320058}],"dockerImageVersionId":31287,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"id":"da84d81a","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":{},"outputs":[],"execution_count":null},{"id":"82fa5459","cell_type":"code","source":"train_labels = pd.read_csv(\"/kaggle/input/stanford-rna-3d-folding-2/train_labels.csv\")\ntrain_sequences = pd.read_csv(\"/kaggle/input/stanford-rna-3d-folding-2/train_sequences.csv\")\nvalidation_labels = pd.read_csv(\"/kaggle/input/stanford-rna-3d-folding-2/validation_labels.csv\")\nvalidation_sequences = pd.read_csv(\"/kaggle/input/stanford-rna-3d-folding-2/validation_sequences.csv\")\ntest_sequences = pd.read_csv(\"/kaggle/input/stanford-rna-3d-folding-2/test_sequences.csv\")\nsample_submission = pd.read_csv(\"/kaggle/input/stanford-rna-3d-folding-2/sample_submission.csv\")\nmsa_folder = \"/kaggle/input/stanford-rna-3d-folding-2/msa/\"\npdb_rna_folder = \"/kaggle/input/stanford-rna-3d-folding-2/pdb_rna/\"\nextra_folder = \"/kaggle/input/stanford-rna-3d-folding-2/extra/\"","metadata":{},"outputs":[],"execution_count":null},{"id":"83bddc9f","cell_type":"code","source":"train_labels","metadata":{},"outputs":[],"execution_count":null},{"id":"04e29df1","cell_type":"code","source":"train_sequences","metadata":{},"outputs":[],"execution_count":null},{"id":"b3d6b26d","cell_type":"code","source":"test_sequences","metadata":{},"outputs":[],"execution_count":null}]}