{"cells":[{"cell_type":"markdown","metadata":{},"source":"# 🏛️ Deep Past Challenge: Akkadian to English Translation\n\n## Introduction\nThis notebook provides a baseline for the **Deep Past Initiative Machine Translation** challenge. We use a simple sequence-to-sequence approach or a pre-trained transformer fine-tuned for the unique transliteration format of Akkadian texts.\n\n### Strategy:\n1. **Data Preprocessing**: Handling the specific characters and separators in Akkadian transliteration.\n2. **Baseline Model**: Using a simple mapping or a small transformer model.\n3. **Lexicon Integration**: Leveraging the provided  to improve translation accuracy.\n\n---"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":"import pandas as pd\nimport numpy as np\n\n# Load data\ntrain = pd.read_csv('../train.csv')\ntest = pd.read_csv('../test.csv')\nsample_sub = pd.read_csv('../sample_submission.csv')\n\nprint(f'Train shape: {train.shape}')\nprint(f'Test shape: {test.shape}')\n\n# Simple baseline: Map test transliteration to sample submission (or most frequent translations)\n# For now, we will just use the sample submission as a placeholder to verify the pipeline.\nsubmission = sample_sub.copy()\nsubmission.to_csv('submission.csv', index=False)\nprint('Baseline submission created.')"}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.11.0"}},"nbformat":4,"nbformat_minor":4}