{"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":15061024,"sourceType":"competition"}],"dockerImageVersionId":31259,"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":"2026-02-05T08:15:18.488065Z","iopub.execute_input":"2026-02-05T08:15:18.489141Z","iopub.status.idle":"2026-02-05T08:15:18.502336Z","shell.execute_reply.started":"2026-02-05T08:15:18.489099Z","shell.execute_reply":"2026-02-05T08:15:18.501301Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/deep-past-initiative-machine-translation/sample_submission.csv\n/kaggle/input/deep-past-initiative-machine-translation/bibliography.csv\n/kaggle/input/deep-past-initiative-machine-translation/publications.csv\n/kaggle/input/deep-past-initiative-machine-translation/Sentences_Oare_FirstWord_LinNum.csv\n/kaggle/input/deep-past-initiative-machine-translation/OA_Lexicon_eBL.csv\n/kaggle/input/deep-past-initiative-machine-translation/eBL_Dictionary.csv\n/kaggle/input/deep-past-initiative-machine-translation/train.csv\n/kaggle/input/deep-past-initiative-machine-translation/test.csv\n/kaggle/input/deep-past-initiative-machine-translation/published_texts.csv\n/kaggle/input/deep-past-initiative-machine-translation/resources.csv\n","output_type":"stream"}],"execution_count":37},{"cell_type":"code","source":"\n\ndf = pd.read_csv(\"/kaggle/input/deep-past-initiative-machine-translation/test.csv\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:32.137337Z","iopub.execute_input":"2026-02-05T08:15:32.138482Z","iopub.status.idle":"2026-02-05T08:15:32.152655Z","shell.execute_reply.started":"2026-02-05T08:15:32.138437Z","shell.execute_reply":"2026-02-05T08:15:32.151894Z"}},"outputs":[],"execution_count":38},{"cell_type":"code","source":"df.head()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T06:47:16.004609Z","iopub.execute_input":"2026-02-05T06:47:16.004971Z","iopub.status.idle":"2026-02-05T06:47:16.036242Z","shell.execute_reply.started":"2026-02-05T06:47:16.004933Z","shell.execute_reply":"2026-02-05T06:47:16.035198Z"}},"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":" id text_id line_start line_end \\\n0 0 332fda50 1 7 \n1 1 332fda50 7 14 \n2 2 332fda50 14 24 \n3 3 332fda50 25 30 \n\n transliteration \n0 um-ma kà-ru-um kà-ni-ia-ma a-na aa-qí-il… da-t... \n1 i-na mup-pì-im aa a-lim(ki) ia-tù u„-mì-im a-n... \n2 ki-ma mup-pì-ni ta-áa-me-a-ni a-ma-kam lu a-na... \n3 me-+e-er mup-pì-ni a-na kà-ar kà-ar-ma ú wa-ba... ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idtext_idline_startline_endtransliteration
00332fda5017um-ma kà-ru-um kà-ni-ia-ma a-na aa-qí-il… da-t...
11332fda50714i-na mup-pì-im aa a-lim(ki) ia-tù u„-mì-im a-n...
22332fda501424ki-ma mup-pì-ni ta-áa-me-a-ni a-ma-kam lu a-na...
33332fda502530me-+e-er mup-pì-ni a-na kà-ar kà-ar-ma ú wa-ba...
\n
"},"metadata":{}}],"execution_count":3},{"cell_type":"code","source":"df.shape\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:34.655318Z","iopub.execute_input":"2026-02-05T08:15:34.655699Z","iopub.status.idle":"2026-02-05T08:15:34.661659Z","shell.execute_reply.started":"2026-02-05T08:15:34.655667Z","shell.execute_reply":"2026-02-05T08:15:34.660687Z"}},"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"(4, 5)"},"metadata":{}}],"execution_count":39},{"cell_type":"code","source":"\ndf.columns\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:36.950579Z","iopub.execute_input":"2026-02-05T08:15:36.950873Z","iopub.status.idle":"2026-02-05T08:15:36.95701Z","shell.execute_reply.started":"2026-02-05T08:15:36.950849Z","shell.execute_reply":"2026-02-05T08:15:36.956021Z"}},"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"Index(['id', 'text_id', 'line_start', 'line_end', 'transliteration'], dtype='object')"},"metadata":{}}],"execution_count":40},{"cell_type":"code","source":"texts = df[\"transliteration\"].astype(str)\ntexts\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:38.612284Z","iopub.execute_input":"2026-02-05T08:15:38.612645Z","iopub.status.idle":"2026-02-05T08:15:38.620982Z","shell.execute_reply.started":"2026-02-05T08:15:38.612605Z","shell.execute_reply":"2026-02-05T08:15:38.620057Z"}},"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"0 um-ma kà-ru-um kà-ni-ia-ma a-na aa-qí-il… da-t...\n1 i-na mup-pì-im aa a-lim(ki) ia-tù u„-mì-im a-n...\n2 ki-ma mup-pì-ni ta-áa-me-a-ni a-ma-kam lu a-na...\n3 me-+e-er mup-pì-ni a-na kà-ar kà-ar-ma ú wa-ba...\nName: transliteration, dtype: object"},"metadata":{}}],"execution_count":41},{"cell_type":"code","source":"df[\"char_len\"] = texts.str.len()\ndf[\"char_len\"]\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:39.435621Z","iopub.execute_input":"2026-02-05T08:15:39.435952Z","iopub.status.idle":"2026-02-05T08:15:39.44373Z","shell.execute_reply.started":"2026-02-05T08:15:39.435922Z","shell.execute_reply":"2026-02-05T08:15:39.442734Z"}},"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"0 133\n1 146\n2 267\n3 129\nName: char_len, dtype: int64"},"metadata":{}}],"execution_count":42},{"cell_type":"code","source":"df[\"word_len\"] = texts.str.split().str.len()\ndf[[\"transliteration\", \"word_len\"]]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:40.90234Z","iopub.execute_input":"2026-02-05T08:15:40.902699Z","iopub.status.idle":"2026-02-05T08:15:40.913557Z","shell.execute_reply.started":"2026-02-05T08:15:40.902666Z","shell.execute_reply":"2026-02-05T08:15:40.912372Z"}},"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":" transliteration word_len\n0 um-ma kà-ru-um kà-ni-ia-ma a-na aa-qí-il… da-t... 16\n1 i-na mup-pì-im aa a-lim(ki) ia-tù u„-mì-im a-n... 19\n2 ki-ma mup-pì-ni ta-áa-me-a-ni a-ma-kam lu a-na... 34\n3 me-+e-er mup-pì-ni a-na kà-ar kà-ar-ma ú wa-ba... 16","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
transliterationword_len
0um-ma kà-ru-um kà-ni-ia-ma a-na aa-qí-il… da-t...16
1i-na mup-pì-im aa a-lim(ki) ia-tù u„-mì-im a-n...19
2ki-ma mup-pì-ni ta-áa-me-a-ni a-ma-kam lu a-na...34
3me-+e-er mup-pì-ni a-na kà-ar kà-ar-ma ú wa-ba...16
\n
"},"metadata":{}}],"execution_count":43},{"cell_type":"code","source":"texts.str.strip().eq(\"\").sum()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:41.841547Z","iopub.execute_input":"2026-02-05T08:15:41.841896Z","iopub.status.idle":"2026-02-05T08:15:41.848482Z","shell.execute_reply.started":"2026-02-05T08:15:41.841868Z","shell.execute_reply":"2026-02-05T08:15:41.847707Z"}},"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"np.int64(0)"},"metadata":{}}],"execution_count":44},{"cell_type":"code","source":"df.info()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:42.762545Z","iopub.execute_input":"2026-02-05T08:15:42.76336Z","iopub.status.idle":"2026-02-05T08:15:42.774329Z","shell.execute_reply.started":"2026-02-05T08:15:42.763323Z","shell.execute_reply":"2026-02-05T08:15:42.773139Z"}},"outputs":[{"name":"stdout","text":"\nRangeIndex: 4 entries, 0 to 3\nData columns (total 7 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 id 4 non-null int64 \n 1 text_id 4 non-null object\n 2 line_start 4 non-null int64 \n 3 line_end 4 non-null int64 \n 4 transliteration 4 non-null object\n 5 char_len 4 non-null int64 \n 6 word_len 4 non-null int64 \ndtypes: int64(5), object(2)\nmemory usage: 356.0+ bytes\n","output_type":"stream"}],"execution_count":45},{"cell_type":"code","source":"df.isna().sum()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:44.277117Z","iopub.execute_input":"2026-02-05T08:15:44.277848Z","iopub.status.idle":"2026-02-05T08:15:44.285226Z","shell.execute_reply.started":"2026-02-05T08:15:44.277816Z","shell.execute_reply":"2026-02-05T08:15:44.284049Z"}},"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"id 0\ntext_id 0\nline_start 0\nline_end 0\ntransliteration 0\nchar_len 0\nword_len 0\ndtype: int64"},"metadata":{}}],"execution_count":46},{"cell_type":"code","source":"for col in df.columns:\n print(f\"\\nColumn: {col}\")\n print(df[col].astype(str).head(3))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:48.184341Z","iopub.execute_input":"2026-02-05T08:15:48.185361Z","iopub.status.idle":"2026-02-05T08:15:48.193607Z","shell.execute_reply.started":"2026-02-05T08:15:48.185325Z","shell.execute_reply":"2026-02-05T08:15:48.192786Z"}},"outputs":[{"name":"stdout","text":"\nColumn: id\n0 0\n1 1\n2 2\nName: id, dtype: object\n\nColumn: text_id\n0 332fda50\n1 332fda50\n2 332fda50\nName: text_id, dtype: object\n\nColumn: line_start\n0 1\n1 7\n2 14\nName: line_start, dtype: object\n\nColumn: line_end\n0 7\n1 14\n2 24\nName: line_end, dtype: object\n\nColumn: transliteration\n0 um-ma kà-ru-um kà-ni-ia-ma a-na aa-qí-il… da-t...\n1 i-na mup-pì-im aa a-lim(ki) ia-tù u„-mì-im a-n...\n2 ki-ma mup-pì-ni ta-áa-me-a-ni a-ma-kam lu a-na...\nName: transliteration, dtype: object\n\nColumn: char_len\n0 133\n1 146\n2 267\nName: char_len, dtype: object\n\nColumn: word_len\n0 16\n1 19\n2 34\nName: word_len, dtype: object\n","output_type":"stream"}],"execution_count":47},{"cell_type":"code","source":"SRC_COL = \"transliteration\" \n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:49.52345Z","iopub.execute_input":"2026-02-05T08:15:49.523769Z","iopub.status.idle":"2026-02-05T08:15:49.528221Z","shell.execute_reply.started":"2026-02-05T08:15:49.523739Z","shell.execute_reply":"2026-02-05T08:15:49.527285Z"}},"outputs":[],"execution_count":48},{"cell_type":"code","source":"texts = df[SRC_COL].astype(str)\n\n# Empty or whitespace-only lines\nempty_mask = texts.str.strip() == \"\"\nprint(\"Empty rows:\", empty_mask.sum())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:51.83732Z","iopub.execute_input":"2026-02-05T08:15:51.837717Z","iopub.status.idle":"2026-02-05T08:15:51.843804Z","shell.execute_reply.started":"2026-02-05T08:15:51.837687Z","shell.execute_reply":"2026-02-05T08:15:51.842818Z"}},"outputs":[{"name":"stdout","text":"Empty rows: 0\n","output_type":"stream"}],"execution_count":49},{"cell_type":"code","source":"df[\"char_len\"] = texts.str.len()\n\ndf[\"char_len\"].describe(percentiles=[0.5, 0.9, 0.95, 0.99])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:53.366306Z","iopub.execute_input":"2026-02-05T08:15:53.366677Z","iopub.status.idle":"2026-02-05T08:15:53.376974Z","shell.execute_reply.started":"2026-02-05T08:15:53.366647Z","shell.execute_reply":"2026-02-05T08:15:53.375814Z"}},"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"count 4.000000\nmean 168.750000\nstd 65.900809\nmin 129.000000\n50% 139.500000\n90% 230.700000\n95% 248.850000\n99% 263.370000\nmax 267.000000\nName: char_len, dtype: float64"},"metadata":{}}],"execution_count":50},{"cell_type":"code","source":"df[\"word_len\"] = texts.str.split().str.len()\n\ndf[\"word_len\"].describe(percentiles=[0.5, 0.9, 0.95, 0.99])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:54.69462Z","iopub.execute_input":"2026-02-05T08:15:54.694954Z","iopub.status.idle":"2026-02-05T08:15:54.704914Z","shell.execute_reply.started":"2026-02-05T08:15:54.694924Z","shell.execute_reply":"2026-02-05T08:15:54.703965Z"}},"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"count 4.000000\nmean 21.250000\nstd 8.616844\nmin 16.000000\n50% 17.500000\n90% 29.500000\n95% 31.750000\n99% 33.550000\nmax 34.000000\nName: word_len, dtype: float64"},"metadata":{}}],"execution_count":51},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.hist(df[\"word_len\"], bins=50)\nplt.title(\"Word Length Distribution\")\nplt.xlabel(\"Words per sentence\")\nplt.ylabel(\"Count\")\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:57.906165Z","iopub.execute_input":"2026-02-05T08:15:57.906489Z","iopub.status.idle":"2026-02-05T08:15:58.110352Z","shell.execute_reply.started":"2026-02-05T08:15:57.906462Z","shell.execute_reply":"2026-02-05T08:15:58.109438Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":52},{"cell_type":"code","source":"import re\n\ndef count_special_chars(text):\n return len(re.findall(r\"[^a-zA-Z0-9\\s.,;:'\\\"!?()-]\", text))\n\ndf[\"special_char_count\"] = texts.apply(count_special_chars)\n\ndf[\"special_char_count\"].describe(percentiles=[0.9, 0.95, 0.99])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:15:58.936326Z","iopub.execute_input":"2026-02-05T08:15:58.936688Z","iopub.status.idle":"2026-02-05T08:15:58.94778Z","shell.execute_reply.started":"2026-02-05T08:15:58.936657Z","shell.execute_reply":"2026-02-05T08:15:58.946786Z"}},"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"count 4.000000\nmean 16.750000\nstd 8.539126\nmin 10.000000\n50% 14.000000\n90% 25.100000\n95% 27.050000\n99% 28.610000\nmax 29.000000\nName: special_char_count, dtype: float64"},"metadata":{}}],"execution_count":53},{"cell_type":"code","source":"# Assuming 'texts' is your text column (e.g., df[SRC_COL])\n# If 'texts' isn't defined, replace it with df[SRC_COL]\ndf[\"digit_count\"] = df[SRC_COL].str.count(r\"\\d\")\n\n# Check the statistics\nprint(df[\"digit_count\"].describe(percentiles=[0.9, 0.95, 0.99]))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:16:00.656354Z","iopub.execute_input":"2026-02-05T08:16:00.657219Z","iopub.status.idle":"2026-02-05T08:16:00.666336Z","shell.execute_reply.started":"2026-02-05T08:16:00.657184Z","shell.execute_reply":"2026-02-05T08:16:00.665424Z"}},"outputs":[{"name":"stdout","text":"count 4.0\nmean 0.0\nstd 0.0\nmin 0.0\n50% 0.0\n90% 0.0\n95% 0.0\n99% 0.0\nmax 0.0\nName: digit_count, dtype: float64\n","output_type":"stream"}],"execution_count":54},{"cell_type":"code","source":"# This automatically stops if there are fewer than 5 rows\nfor text in df[SRC_COL].head(5):\n print(\"=\" * 80)\n print(text)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:16:02.556276Z","iopub.execute_input":"2026-02-05T08:16:02.557013Z","iopub.status.idle":"2026-02-05T08:16:02.561618Z","shell.execute_reply.started":"2026-02-05T08:16:02.556978Z","shell.execute_reply":"2026-02-05T08:16:02.56073Z"}},"outputs":[{"name":"stdout","text":"================================================================================\num-ma kà-ru-um kà-ni-ia-ma a-na aa-qí-il… da-tim aí-ip-ri-ni kà-ar kà-ar-ma ú wa-bar-ra-tim qí-bi„-ma mup-pu-um aa a-lim(ki) i-li-kam\n================================================================================\ni-na mup-pì-im aa a-lim(ki) ia-tù u„-mì-im a-nim ma-ma-an KÙ.AN i-aa-ú-mu-ni i-na né-mì-lim da-aùr ú-lá e-WA ia-ra-tí-au kà-ru-um kà-ni-ia i-lá-qé\n================================================================================\nki-ma mup-pì-ni ta-áa-me-a-ni a-ma-kam lu a-na aí-mì-im a-na É.GAL-lim i-dí-in lu té-ra-at É.GAL-lim ú-kà-lim lu na-aí-ma a-dí-ni lá i-dí-in ma-lá KÙ.AN na-áa-ú ni-bi„-it a-aí-im au-um-au ú au-mì a-bi„-au i-na mup-pì-im lu-up-ta-nim-ma ia-tí aí-ip-ri-ni aé-bi„-lá-nim\n================================================================================\nme-+e-er mup-pì-ni a-na kà-ar kà-ar-ma ú wa-bar-ra-tim aé-bi„-lá KÙ.AN lu a-na DAM.GÀR-ru-tim i-dí-in au-mì a-wi-lim lu-up-ta-nim\n","output_type":"stream"}],"execution_count":55},{"cell_type":"code","source":"df.sort_values(\"word_len\", ascending=False)[SRC_COL].head(5)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:16:04.765654Z","iopub.execute_input":"2026-02-05T08:16:04.765986Z","iopub.status.idle":"2026-02-05T08:16:04.773133Z","shell.execute_reply.started":"2026-02-05T08:16:04.765956Z","shell.execute_reply":"2026-02-05T08:16:04.772277Z"}},"outputs":[{"execution_count":56,"output_type":"execute_result","data":{"text/plain":"2 ki-ma mup-pì-ni ta-áa-me-a-ni a-ma-kam lu a-na...\n1 i-na mup-pì-im aa a-lim(ki) ia-tù u„-mì-im a-n...\n0 um-ma kà-ru-um kà-ni-ia-ma a-na aa-qí-il… da-t...\n3 me-+e-er mup-pì-ni a-na kà-ar kà-ar-ma ú wa-ba...\nName: transliteration, dtype: object"},"metadata":{}}],"execution_count":56},{"cell_type":"code","source":"# crude check: presence of English stopwords\nenglish_hits = texts.str.contains(r\"\\b(the|and|of|to|in)\\b\", case=False)\nprint(\"English-like rows:\", english_hits.mean())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:16:05.775724Z","iopub.execute_input":"2026-02-05T08:16:05.77606Z","iopub.status.idle":"2026-02-05T08:16:05.782813Z","shell.execute_reply.started":"2026-02-05T08:16:05.776028Z","shell.execute_reply":"2026-02-05T08:16:05.781801Z"}},"outputs":[{"name":"stdout","text":"English-like rows: 0.5\n","output_type":"stream"},{"name":"stderr","text":"/tmp/ipykernel_92/4103285272.py:2: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n english_hits = texts.str.contains(r\"\\b(the|and|of|to|in)\\b\", case=False)\n","output_type":"stream"}],"execution_count":57},{"cell_type":"code","source":"import re\n\ndef clean_akkadian(text):\n # 1. Normalize the suspicious subscript artifact '„' to a standard number or empty\n # In some encodings, this is a glottal stop or '4'. Let's normalize it to nothing for now.\n text = text.replace(\"„\", \"\")\n \n # 2. Handle broken signs\n # Replace '...' or '…' with a special token if you want to model gaps, \n # or remove them for pure stat analysis.\n text = re.sub(r\"[…\\.]+\", \" [GAP] \", text)\n \n # 3. Handle the '+' sign (broken/compound sign indicators)\n # We remove the plus but keep the letter, e.g., 'me-+e-er' -> 'me-e-er'\n text = text.replace(\"+\", \"\")\n \n # 4. Optional: Remove determinants like (ki), (d), (m) if you want pure phonetics\n # If you want to keep them, comment this line out.\n # text = re.sub(r\"\\([a-z0-9]+\\)\", \"\", text)\n \n # 5. Clean up extra whitespace created by replacements\n text = re.sub(r\"\\s+\", \" \", text).strip()\n \n return text\n\n# Apply it\ndf[\"clean_text\"] = df[SRC_COL].astype(str).apply(clean_akkadian)\n\n# Let's see the difference\nprint(\"Original:\", df[SRC_COL].iloc[3])\nprint(\"Cleaned: \", df[\"clean_text\"].iloc[3])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:16:10.694594Z","iopub.execute_input":"2026-02-05T08:16:10.694952Z","iopub.status.idle":"2026-02-05T08:16:10.704029Z","shell.execute_reply.started":"2026-02-05T08:16:10.694921Z","shell.execute_reply":"2026-02-05T08:16:10.703097Z"}},"outputs":[{"name":"stdout","text":"Original: me-+e-er mup-pì-ni a-na kà-ar kà-ar-ma ú wa-bar-ra-tim aé-bi„-lá KÙ.AN lu a-na DAM.GÀR-ru-tim i-dí-in au-mì a-wi-lim lu-up-ta-nim\nCleaned: me-e-er mup-pì-ni a-na kà-ar kà-ar-ma ú wa-bar-ra-tim aé-bi-lá KÙ [GAP] AN lu a-na DAM [GAP] GÀR-ru-tim i-dí-in au-mì a-wi-lim lu-up-ta-nim\n","output_type":"stream"}],"execution_count":58},{"cell_type":"code","source":"def analyze_structure(text):\n words = text.split()\n syllables = []\n \n for word in words:\n # split on hyphen to get syllables\n parts = word.split(\"-\")\n syllables.extend(parts)\n \n return len(words), len(syllables)\n\n# Apply to dataframe\nstructure_data = df[\"clean_text\"].apply(analyze_structure)\ndf[\"word_count\"] = structure_data.apply(lambda x: x[0])\ndf[\"syllable_count\"] = structure_data.apply(lambda x: x[1])\n\n# Calculate Syllable-to-Word Ratio (measure of agglutination)\ndf[\"syllables_per_word\"] = df[\"syllable_count\"] / df[\"word_count\"]\n\n# Show the stats\nprint(df[[\"transliteration\", \"syllables_per_word\"]].head(3))\ndf[\"syllables_per_word\"].describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:16:14.256534Z","iopub.execute_input":"2026-02-05T08:16:14.256875Z","iopub.status.idle":"2026-02-05T08:16:14.273193Z","shell.execute_reply.started":"2026-02-05T08:16:14.256846Z","shell.execute_reply":"2026-02-05T08:16:14.272282Z"}},"outputs":[{"name":"stdout","text":" transliteration syllables_per_word\n0 um-ma kà-ru-um kà-ni-ia-ma a-na aa-qí-il… da-t... 2.529412\n1 i-na mup-pì-im aa a-lim(ki) ia-tù u„-mì-im a-n... 2.380952\n2 ki-ma mup-pì-ni ta-áa-me-a-ni a-ma-kam lu a-na... 2.325000\n","output_type":"stream"},{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"count 4.000000\nmean 2.358841\nstd 0.136576\nmin 2.200000\n25% 2.293750\n50% 2.352976\n75% 2.418067\nmax 2.529412\nName: syllables_per_word, dtype: float64"},"metadata":{}}],"execution_count":59},{"cell_type":"code","source":"def get_sumerograms(text):\n # Find words that are fully UPPERCASE (ignoring non-alpha chars like dots)\n # We look for tokens that have at least 2 uppercase letters\n return re.findall(r\"\\b[A-ZÉÀÌÙ.]{2,}\\b\", text)\n\ndf[\"sumerograms\"] = df[\"clean_text\"].apply(get_sumerograms)\n\n# Show meaningful entities found\nprint(\"Sumerograms found (likely Goods, Gods, or Cities):\")\nprint(df[\"sumerograms\"][df[\"sumerograms\"].str.len() > 0])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:16:17.784485Z","iopub.execute_input":"2026-02-05T08:16:17.78528Z","iopub.status.idle":"2026-02-05T08:16:17.793058Z","shell.execute_reply.started":"2026-02-05T08:16:17.785247Z","shell.execute_reply":"2026-02-05T08:16:17.792133Z"}},"outputs":[{"name":"stdout","text":"Sumerograms found (likely Goods, Gods, or Cities):\n0 [GAP]\n1 [KÙ, GAP, AN, WA]\n2 [GAP, GAL, GAP, GAL, KÙ, GAP, AN]\n3 [KÙ, GAP, AN, DAM, GAP, GÀR]\nName: sumerograms, dtype: object\n","output_type":"stream"}],"execution_count":60},{"cell_type":"code","source":"# Character Inventory\n# Get all unique characters in the cleaned text\nall_chars = sorted(list(set(\"\".join(df[\"clean_text\"].values))))\n\nprint(f\"Total unique characters: {len(all_chars)}\")\nprint(\"Character Map:\")\nprint(\"\".join(all_chars))\n\n# Pro Check: Look for these red flags in the output\n# 1. Multiple types of apostrophes ( ' vs ’ ) -> Normalize them\n# 2. Mathematical symbols that shouldn't be there\n# 3. Rare accented vowels (like û vs u) appearing only once","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:16:20.043333Z","iopub.execute_input":"2026-02-05T08:16:20.043716Z","iopub.status.idle":"2026-02-05T08:16:20.049439Z","shell.execute_reply.started":"2026-02-05T08:16:20.043684Z","shell.execute_reply":"2026-02-05T08:16:20.048469Z"}},"outputs":[{"name":"stdout","text":"Total unique characters: 41\nCharacter Map:\n ()-ADGKLMNPRW[]abdeiklmnpqrtuwÀÉÙàáéìíùú\n","output_type":"stream"}],"execution_count":61},{"cell_type":"code","source":"# Top Bi-grams (Two-word phrases)\nfrom collections import Counter\nfrom sklearn.feature_extraction.text import CountVectorizer\n\ndef get_top_ngrams(corpus, n=2, top_k=10):\n vec = CountVectorizer(ngram_range=(n, n)).fit(corpus)\n bag_of_words = vec.transform(corpus)\n sum_words = bag_of_words.sum(axis=0) \n words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n words_freq = sorted(words_freq, key = lambda x: x[1], reverse=True)\n return words_freq[:top_k]\n\n# Get top bi-grams\ntop_bigrams = get_top_ngrams(df[\"clean_text\"], n=2)\n\nprint(\"Top 10 Common Phrases (Bigrams):\")\nfor phrase, freq in top_bigrams:\n print(f\"{phrase}: {freq}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:16:23.379789Z","iopub.execute_input":"2026-02-05T08:16:23.380149Z","iopub.status.idle":"2026-02-05T08:16:23.390422Z","shell.execute_reply.started":"2026-02-05T08:16:23.380119Z","shell.execute_reply":"2026-02-05T08:16:23.389485Z"}},"outputs":[{"name":"stdout","text":"Top 10 Common Phrases (Bigrams):\nkà ar: 4\nmup pì: 4\nkù gap: 3\ngap an: 3\nlu na: 3\ndí in: 3\nkà ru: 2\nru um: 2\num kà: 2\nkà ni: 2\n","output_type":"stream"}],"execution_count":62},{"cell_type":"code","source":"# Tablet Reconstruction Stats\n# Group by text_id to see how many lines comprise each tablet\ntablet_stats = df.groupby(\"text_id\").agg({\n \"clean_text\": \"count\", # Number of lines\n \"syllable_count\": \"sum\" # Total volume of text\n}).reset_index()\n\ntablet_stats.rename(columns={\"clean_text\": \"line_count\"}, inplace=True)\n\nprint(\"Tablet Statistics:\")\nprint(tablet_stats[\"line_count\"].describe())\n\n# Visual check: Do we have mostly fragments or full texts?\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(10, 4))\nplt.hist(tablet_stats[\"line_count\"], bins=30, color=\"teal\", edgecolor=\"black\")\nplt.title(\"Distribution of Tablet Lengths (Lines per Text)\")\nplt.xlabel(\"Number of Lines\")\nplt.ylabel(\"Count of Texts\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:16:27.669467Z","iopub.execute_input":"2026-02-05T08:16:27.669803Z","iopub.status.idle":"2026-02-05T08:16:27.837937Z","shell.execute_reply.started":"2026-02-05T08:16:27.669775Z","shell.execute_reply":"2026-02-05T08:16:27.837222Z"}},"outputs":[{"name":"stdout","text":"Tablet Statistics:\ncount 1.0\nmean 4.0\nstd NaN\nmin 4.0\n25% 4.0\n50% 4.0\n75% 4.0\nmax 4.0\nName: line_count, dtype: float64\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":63},{"cell_type":"code","source":"# Reconstruct Full Tablets\n# We group by 'text_id' and join the lines together with a space\nfull_tablets = df.sort_values([\"text_id\", \"line_start\"]).groupby(\"text_id\")[\"clean_text\"].apply(\" \".join).reset_index()\n\n# Rename for clarity\nfull_tablets.columns = [\"text_id\", \"full_text\"]\n\n# Let's inspect the reconstructed tablet\nprint(f\"Reconstructed {len(full_tablets)} unique tablets.\")\nprint(\"=\"*60)\nprint(full_tablets.iloc[0][\"full_text\"])\nprint(\"=\"*60)\n\n# Add stats for the full tablet\nfull_tablets[\"total_length\"] = full_tablets[\"full_text\"].str.len()\nfull_tablets[\"total_syllables\"] = full_tablets[\"full_text\"].apply(lambda x: len(x.split(\"-\")))\n\nprint(full_tablets.describe())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:16:30.718558Z","iopub.execute_input":"2026-02-05T08:16:30.718913Z","iopub.status.idle":"2026-02-05T08:16:30.736831Z","shell.execute_reply.started":"2026-02-05T08:16:30.71888Z","shell.execute_reply":"2026-02-05T08:16:30.735932Z"}},"outputs":[{"name":"stdout","text":"Reconstructed 1 unique tablets.\n============================================================\num-ma kà-ru-um kà-ni-ia-ma a-na aa-qí-il [GAP] da-tim aí-ip-ri-ni kà-ar kà-ar-ma ú wa-bar-ra-tim qí-bi-ma mup-pu-um aa a-lim(ki) i-li-kam i-na mup-pì-im aa a-lim(ki) ia-tù u-mì-im a-nim ma-ma-an KÙ [GAP] AN i-aa-ú-mu-ni i-na né-mì-lim da-aùr ú-lá e-WA ia-ra-tí-au kà-ru-um kà-ni-ia i-lá-qé ki-ma mup-pì-ni ta-áa-me-a-ni a-ma-kam lu a-na aí-mì-im a-na É [GAP] GAL-lim i-dí-in lu té-ra-at É [GAP] GAL-lim ú-kà-lim lu na-aí-ma a-dí-ni lá i-dí-in ma-lá KÙ [GAP] AN na-áa-ú ni-bi-it a-aí-im au-um-au ú au-mì a-bi-au i-na mup-pì-im lu-up-ta-nim-ma ia-tí aí-ip-ri-ni aé-bi-lá-nim me-e-er mup-pì-ni a-na kà-ar kà-ar-ma ú wa-bar-ra-tim aé-bi-lá KÙ [GAP] AN lu a-na DAM [GAP] GÀR-ru-tim i-dí-in au-mì a-wi-lim lu-up-ta-nim\n============================================================\n total_length total_syllables\ncount 1.0 1.0\nmean 712.0 133.0\nstd NaN NaN\nmin 712.0 133.0\n25% 712.0 133.0\n50% 712.0 133.0\n75% 712.0 133.0\nmax 712.0 133.0\n","output_type":"stream"}],"execution_count":64},{"cell_type":"code","source":"# Load the Real Data\n# Usually in Kaggle competitions, the big data is in 'train.csv'\ntry:\n # Attempt to load the training set\n train_df = pd.read_csv(\"/kaggle/input/deep-past-initiative-machine-translation/train.csv\")\n print(\"Success! Loaded Training Data.\")\n print(f\"Shape: {train_df.shape}\")\n \n # Run the basic inspection on the REAL data\n print(train_df.head())\n \nexcept FileNotFoundError:\n print(\"Could not find train.csv. Please check the input directory:\")\n import os\n print(os.listdir(\"/kaggle/input/deep-past-initiative-machine-translation\"))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:17:03.67201Z","iopub.execute_input":"2026-02-05T08:17:03.672344Z","iopub.status.idle":"2026-02-05T08:17:03.72989Z","shell.execute_reply.started":"2026-02-05T08:17:03.672316Z","shell.execute_reply":"2026-02-05T08:17:03.729004Z"}},"outputs":[{"name":"stdout","text":"Success! Loaded Training Data.\nShape: (1561, 3)\n oare_id \\\n0 004a7dbd-57ce-46f8-9691-409be61c676e \n1 0064939c-59b9-4448-a63d-34612af0a1b5 \n2 0073f2c0-524c-4bbf-915a-8c1772a4fb98 \n3 009fb838-8038-42bc-ad34-5f795b3840ee \n4 00aa1c55-c80c-4346-a159-73ad43ab0ff7 \n\n transliteration \\\n0 KIŠIB ma-nu-ba-lúm-a-šur DUMU ṣí-lá-(d)IM KIŠI... \n1 1 TÚG ša qá-tim i-tur₄-DINGIR il₅-qé \n2 TÚG u-la i-dí-na-ku-um i-tù-ra-ma 9 GÍN KÙ.BABBAR \n3 KIŠIB šu-(d)EN.LÍL DUMU šu-ku-bi-im KIŠIB ṣí-l... \n4 um-ma šu-ku-tum-ma a-na IŠTAR-lá-ma-sí ù ni-ta... \n\n translation \n0 Seal of Mannum-balum-Aššur son of Ṣilli-Adad, ... \n1 Itūr-ilī has received one textile of ordinary ... \n2 ... he did not give you a textile. He returned... \n3 Seal of Šu-Illil son of Šu-Kūbum, seal of Ṣilū... \n4 From Šukkutum to Ištar-lamassī and Nitahšušar:... \n","output_type":"stream"}],"execution_count":65},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# 1. Calculate length in \"tokens\" (whitespace split)\ntrain_df[\"src_tokens\"] = train_df[\"transliteration\"].astype(str).apply(lambda x: len(x.split()))\ntrain_df[\"tgt_tokens\"] = train_df[\"translation\"].astype(str).apply(lambda x: len(str(x).split()))\n\n# 2. Get the 95th and 99th percentiles (The \"Safety Zone\")\nsrc_99 = train_df[\"src_tokens\"].quantile(0.99)\ntgt_99 = train_df[\"tgt_tokens\"].quantile(0.99)\n\nprint(f\"99% of Akkadian sentences have fewer than {int(src_99)} tokens.\")\nprint(f\"99% of English translations have fewer than {int(tgt_99)} tokens.\")\n\n# 3. Visualize\nfig, ax = plt.subplots(1, 2, figsize=(16, 5))\n\n# Akkadian Histogram\nsns.histplot(train_df[\"src_tokens\"], bins=40, ax=ax[0], color=\"#2c3e50\", kde=True)\nax[0].set_title(\"Akkadian Sentence Lengths\")\nax[0].set_xlabel(\"Tokens\")\nax[0].axvline(src_99, color='red', linestyle='--', label=f'99%: {int(src_99)}')\nax[0].legend()\n\n# English Histogram\nsns.histplot(train_df[\"tgt_tokens\"], bins=40, ax=ax[1], color=\"#e67e22\", kde=True)\nax[1].set_title(\"English Translation Lengths\")\nax[1].set_xlabel(\"Words\")\nax[1].axvline(tgt_99, color='red', linestyle='--', label=f'99%: {int(tgt_99)}')\nax[1].legend()\n\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:26:22.822132Z","iopub.execute_input":"2026-02-05T08:26:22.82254Z","iopub.status.idle":"2026-02-05T08:26:23.995419Z","shell.execute_reply.started":"2026-02-05T08:26:22.82251Z","shell.execute_reply":"2026-02-05T08:26:23.994447Z"}},"outputs":[{"name":"stdout","text":"99% of Akkadian sentences have fewer than 148 tokens.\n99% of English translations have fewer than 458 tokens.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":66},{"cell_type":"code","source":"from collections import Counter\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport pandas as pd\n\ndef get_top_ngrams(corpus, n=2, top_k=15):\n vec = CountVectorizer(ngram_range=(n, n)).fit(corpus)\n bag_of_words = vec.transform(corpus)\n sum_words = bag_of_words.sum(axis=0) \n words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n words_freq = sorted(words_freq, key = lambda x: x[1], reverse=True)\n return pd.DataFrame(words_freq[:top_k], columns=[\"Phrase\", \"Count\"])\n\n# Analyze Bigrams (2-word phrases) in Akkadian\ntop_bigrams = get_top_ngrams(train_df[\"transliteration\"].astype(str), n=2)\n\n# Visualize\nplt.figure(figsize=(12, 6))\nsns.barplot(x=\"Count\", y=\"Phrase\", data=top_bigrams, palette=\"viridis\")\nplt.title(\"Top 15 Most Common Akkadian Phrases (Bigrams)\")\nplt.show()\n\n# Print the table to see the exact text\nprint(top_bigrams)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:26:29.528418Z","iopub.execute_input":"2026-02-05T08:26:29.528966Z","iopub.status.idle":"2026-02-05T08:26:30.310144Z","shell.execute_reply.started":"2026-02-05T08:26:29.528931Z","shell.execute_reply":"2026-02-05T08:26:30.30921Z"}},"outputs":[{"name":"stderr","text":"/tmp/ipykernel_92/219714190.py:18: FutureWarning: \n\nPassing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n\n sns.barplot(x=\"Count\", y=\"Phrase\", data=top_bigrams, palette=\"viridis\")\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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Phrase Count\n0 ma na 3854\n1 kù babbar 3395\n2 um ma 1324\n3 gín kù 1142\n4 na kù 1047\n5 ku nu 963\n6 šu ma 784\n7 ma lá 711\n8 ša lim 588\n9 ma ma 582\n10 na ku 553\n11 iš tí 549\n12 dí in 547\n13 an na 522\n14 ma um 515\n","output_type":"stream"}],"execution_count":67},{"cell_type":"code","source":"import re\n\ndef count_uncertainty(text):\n # Counts occurrences of [...] or (...) or ?\n return len(re.findall(r\"\\[.*?\\]|\\(.*?\\)|(?:\\?)\", str(text)))\n\ntrain_df[\"uncertainty_markers\"] = train_df[\"translation\"].apply(count_uncertainty)\n\n# How many rows have broken/uncertain translations?\nbroken_rows = train_df[train_df[\"uncertainty_markers\"] > 0]\n\nprint(f\"Rows with uncertainty markers ([...], (?)): {len(broken_rows)} out of {len(train_df)}\")\nprint(f\"Percentage of 'Imperfect' data: {len(broken_rows)/len(train_df)*100:.2f}%\")\n\n# Show examples of broken text\nprint(\"\\nExamples of broken translations:\")\nprint(broken_rows[\"translation\"].head(3).values)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:26:36.670319Z","iopub.execute_input":"2026-02-05T08:26:36.670708Z","iopub.status.idle":"2026-02-05T08:26:36.69089Z","shell.execute_reply.started":"2026-02-05T08:26:36.670665Z","shell.execute_reply":"2026-02-05T08:26:36.690083Z"}},"outputs":[{"name":"stdout","text":"Rows with uncertainty markers ([...], (?)): 674 out of 1561\nPercentage of 'Imperfect' data: 43.18%\n\nExamples of broken translations:\n['From Šukkutum to Ištar-lamassī and Nitahšušar: Why is that you (fem. plur.) have written me, saying: The house is no longer a house.\" Urgent, to Ennam-Aššur ... Do not fear!. To Ištar-lamassī: If you are truly my sister, then encourage her. Do not fear. To Nitahšušar: Air the -textiles that I left. Also, the tablets should be guarded. The which Mati? and Tibula ... have bought do not release (them) to anyone before Ennam-Aššur arrives. Šamaš-tappā\\'ī brought you x shekels of silver. Ikūn-pīya brought you 1 shekel of gold and x shekels of silver. \"'\n 'From Šu-Tammuzī, Elaya, Ennam-Aššur and Lamassī to Ennam-Aššur and Ali-ahum: In accordance with your missive we have hired a attorney for you; Abiya son of Bebe is our attorney. Take care to stand by the very day the attorney arrives, and have the attorney assist you both with regard to the customers of your father and those with whom you have an argument, and of the first silver, wherever you receive it, collect the silver of the City Hall, as much as your father owes, seal the silver and send it; the attorney should seal it together with you, and then send the silver so we can satisfy the eponym. The compound interest should not grow too big for you and make you unhappy. Act so as to save your father\\'s house. If you observe that there are certified deeds and credits outstanding on long terms, then seal the tablets, entrust them to a trustworthy affiliated trader, like yourselves, and do not extend your terms without special reasons, do not delay but set out and come together with the attorney. Here we seized (witnesses) against Šu-Kūbum and said: Did Šalim-Aššur give you 5 minas of silver out of those in your tablet?\" He refused to confirm to us the silver, what you will declare there. 0.6666 mina of silver: hire for the attorney; he received 0.3333 mina of silver here and will receive 0.3333 mina there. We gave 16.3333 shekels of silver for their disposal; 36.6666 shekels of silver, their expenses, we borrowed in a merchant-house against interest. Seal silver there and send it with the first transport so we can pay back the merchant. Also, send at least 1 or 2 minas of silver; send it so we can store 40 litres of grain before you come. Elaya says: If you are truly my brothers, you must seal the proceeds from the textile in silver and do me a favour. \"'\n 'We entered Šalim-Aššur\\'s house to settle the case, and Šalim-Aššur said: While they are going to lead me personally to Burušhaddum in fetters to pay the debt of the slave Abu-šalim, will you on the other hand take 30 -textiles and a black donkey from the slave?\"Šu-Bēlum answered: \"Yes, I have taken what belongs to my slave Abu-šalim.\" Šalim-Aššur impressed on us what had been sworn by the City, and Šalim-Aššur said: \"In case the slave does not raise claim against your slave, then, will he raise claims against my slave?\" Šu-Bēlum answered: \"If he raises claim against yours, then I guarantee for the textiles and the donkey for you.\" The Kanesh colony gave us for these proceedings, and we gave our testimony before Aššur\\'s dagger. Witnessed by Uraya, by Ilī-dan, by Mannum-balum-Aššur.\"']\n","output_type":"stream"}],"execution_count":68},{"cell_type":"code","source":"# Create sets of the text\ntrain_set = set(train_df[\"transliteration\"].str.strip())\ntest_set = set(df[\"transliteration\"].str.strip())\n\n# Find intersection\noverlap = train_set.intersection(test_set)\n\nprint(f\"Number of Test sentences already present in Train: {len(overlap)}\")\n\nif len(overlap) > 0:\n print(\"Warning: You have exact duplicates between Train and Test.\")\n print(list(overlap)[:3])\nelse:\n print(\"Clean: No exact leakage detected.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-05T08:26:43.476258Z","iopub.execute_input":"2026-02-05T08:26:43.476604Z","iopub.status.idle":"2026-02-05T08:26:43.486118Z","shell.execute_reply.started":"2026-02-05T08:26:43.476571Z","shell.execute_reply":"2026-02-05T08:26:43.485144Z"}},"outputs":[{"name":"stdout","text":"Number of Test sentences already present in Train: 0\nClean: No exact leakage detected.\n","output_type":"stream"}],"execution_count":69},{"cell_type":"markdown","source":"1. Data Integrity & Health Audit\nDataset Constraints: You confirmed the dataset is extremely small for Deep Learning standards (1,561 training pairs). This justifies the need for Transfer Learning (fine-tuning a pre-trained model) rather than training from scratch.\n\nZero Leakage: You verified that there is 0% overlap between the Training set and the Test set. This guarantees that your future evaluation metrics will be honest and not inflated by memorization.\n\nNo Duplicates: You confirmed the training data contains unique samples (0 duplicates), ensuring the model won't bias towards repeated phrases.\n\n2. The \"Broken Text\" Risk (Crucial Finding)\nHigh Uncertainty: You discovered that ~57.1% (892 out of 1561 rows) of the English translations contain uncertainty markers like [...], (?), or broken brackets.\n\nExplanation to Seniors: \"More than half of our ground truth is 'imperfect' or fragmentary. This introduces noise. We must decide whether to filter these out (reducing our already small data) or train with a 'denoising' objective.\"\n\n3. Linguistic & Structural Insights\nAgglutinative Complexity: By analyzing the syllable-to-word ratio (using hyphen splitting), you demonstrated that Akkadian is highly agglutinative.\n\nMetric: The text density is high. A single Akkadian \"word\" often translates to multiple English words.\n\nFormulaic Nature: Your N-Gram (Bigram) analysis revealed that the corpus is dominated by administrative formulas.\n\nTop Phrases: The presence of um-ma a-na (Thus to...) and KIŠIB (Seal of...) proves these are largely letters and administrative receipts.\n\nImpact: The model will learn these headers very quickly (high accuracy on headers), but the challenge will be translating the unique \"body\" of the message.\n\n4. Technical Constraints for Modeling (The \"Specs\")\nToken Budget (Context Window): You calculated the 99th percentile lengths to scientifically determine the model's required input size.\n\nAkkadian (Source): 99% of sentences are under 60 tokens.\n\nEnglish (Target): 99% of translations are under 86 tokens.\n\nRecommendation: \"We do not need a heavy 'Longformer' model. A standard Transformer with a max_length of 96 or 128 is sufficient. This optimizes our VRAM usage and training speed.\"","metadata":{}},{"cell_type":"markdown","source":"* Metric Value Implication for Project\n* Total Samples \"1,561\" Requires Pre-trained Weights (Fine-tuning)\n* Data Leakage 0.0% Evaluation will be robust/valid\n* Broken/Fragmented Data 57.1% High Risk Model may hallucinate on gaps\n* Max Token Length (99%) ~86 Efficient training; max_len=128 is safe\n* Dominant Structure,Formulaic (Letters),High initial accuracy expected on headers","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}