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Deep Past Initiative – Machine Translation (Training Notebook)\n\nThis notebook is copied and edited from: [DPC Starter Train](https://www.kaggle.com/code/takamichitoda/dpc-starter-train)\n","metadata":{}},{"cell_type":"code","source":"!pip install evaluate sacrebleu\n!pip install evaluate","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2026-03-26T14:34:34.691733Z","iopub.execute_input":"2026-03-26T14:34:34.691924Z","iopub.status.idle":"2026-03-26T14:34:42.874699Z","shell.execute_reply.started":"2026-03-26T14:34:34.691905Z","shell.execute_reply":"2026-03-26T14:34:42.873602Z"},"papermill":{"duration":5.195469,"end_time":"2025-12-18T06:39:02.528153","exception":false,"start_time":"2025-12-18T06:38:57.332684","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport gc\nimport re\nimport pandas as pd\nimport numpy as np\nimport torch\nfrom sklearn.model_selection import train_test_split\nfrom datasets import Dataset\nfrom transformers import (\n AutoTokenizer, \n AutoModelForSeq2SeqLM, \n DataCollatorForSeq2Seq, \n Seq2SeqTrainingArguments, \n Seq2SeqTrainer\n)\nfrom sentence_transformers import SentenceTransformer, util\nimport evaluate\nimport sacrebleu\nimport math","metadata":{"execution":{"iopub.status.busy":"2026-03-26T14:34:42.876875Z","iopub.execute_input":"2026-03-26T14:34:42.877174Z","iopub.status.idle":"2026-03-26T14:35:12.850193Z","shell.execute_reply.started":"2026-03-26T14:34:42.877145Z","shell.execute_reply":"2026-03-26T14:35:12.849597Z"},"papermill":{"duration":32.26918,"end_time":"2025-12-18T06:39:34.800016","exception":false,"start_time":"2025-12-18T06:39:02.530836","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class Config:\n # Akkadian transliteration contains a lot of noise and many unknown words, so\n # ByT5, which processes text at the character (byte) level rather than the word level, is the strongest choice.\n MODEL_NAME = \"google/byt5-small\" \n \n # ByT5 tends to produce longer token sequences, but 512 tokens is enough at the sentence level.\n MAX_LENGTH = 512\n \n BATCH_SIZE = 16 # Adjust depending on GPU memory (on a P100 you can usually go with 8–16).\n EPOCHS = 20\n LEARNING_RATE = 2e-4\n OUTPUT_DIR = \"./byt5-akkadian-model\"","metadata":{"execution":{"iopub.status.busy":"2026-03-26T14:35:12.851091Z","iopub.execute_input":"2026-03-26T14:35:12.851806Z","iopub.status.idle":"2026-03-26T14:35:12.855975Z","shell.execute_reply.started":"2026-03-26T14:35:12.851764Z","shell.execute_reply":"2026-03-26T14:35:12.855212Z"},"papermill":{"duration":0.008757,"end_time":"2025-12-18T06:39:34.811471","exception":false,"start_time":"2025-12-18T06:39:34.802714","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Compiled regex patterns:","metadata":{}},{"cell_type":"code","source":"class CompiledPatterns:\n \"\"\"Pre-compile all regex patterns once at import time\"\"\"\n \n # Input preprocessing\n BIG_GAP_INPUT = re.compile(r'(\\.{3,}|…+|……)')\n SMALL_GAP_INPUT = re.compile(r'(xx+|\\s+x\\s+)')\n \n # Output postprocessing\n AKKADIAN_GAP = re.compile(r'(\\[x\\]|\\(x\\)|\\bx\\b)', re.I)\n BIG_GAP_OUTPUT = re.compile(r'(\\.{3,}|…|\\[\\.+\\])')\n MERGE_GAPS = re.compile(r'\\s*')\n MERGE_BIG_GAPS = re.compile(r'\\s*')\n GRAMMAR_ANNOTATIONS = re.compile(\n r'\\((fem|plur|pl|sing|singular|plural|\\?|!)\\.?\\s*\\w*\\)', re.I\n )\n REPEATED_WORDS = re.compile(r'\\b(\\w+)(?:\\s+\\1\\b)+')\n SPACE_BEFORE_PUNCT = re.compile(r'\\s+([.,:])')\n REPEATED_PUNCT = re.compile(r'([.,])\\1+')\n MULTI_SPACE = re.compile(r'\\s+')\n \n # N-gram dedup (pre-compiled for sizes 4,3,2)\n NGRAM_PATTERNS = [\n re.compile(r'\\b((?:\\w+\\s+){' + str(n-1) + r'}\\w+)(?:\\s+\\1\\b)+')\n for n in range(4, 1, -1)\n ]\n \n # Fraction patterns (KEEP QUIRKY VALUES!)\n FRACTION_PATTERNS = [\n (re.compile(r'(\\d+)\\.5\\b'), r'\\1 ½'),\n (re.compile(r'(\\d+)\\.25\\b'), r'\\1 ¼'),\n (re.compile(r'(\\d+)\\.75\\b'), r'\\1 ¾'),\n (re.compile(r'(\\d+)\\.33+\\d*\\b'), r'\\1 ⅓'), # Quirky - kept as-is\n (re.compile(r'(\\d+)\\.66+\\d*\\b'), r'\\1 ⅔'), # Quirky - kept as-is\n (re.compile(r'\\b0\\.5\\b'), '½'),\n (re.compile(r'\\b0\\.25\\b'), '¼'),\n (re.compile(r'\\b0\\.75\\b'), '¾'),\n (re.compile(r'\\b0\\.33+\\d*\\b'), '⅓'),\n (re.compile(r'\\b0\\.66+\\d*\\b'), '⅔'),\n ]\n\nPATTERNS = CompiledPatterns()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-26T14:35:12.857085Z","iopub.execute_input":"2026-03-26T14:35:12.857393Z","iopub.status.idle":"2026-03-26T14:35:12.906813Z","shell.execute_reply.started":"2026-03-26T14:35:12.857362Z","shell.execute_reply":"2026-03-26T14:35:12.906027Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Preprocess and postprocess functions:","metadata":{}},{"cell_type":"code","source":"# Own code, with assistance from Google Gemini\ndef implement_lexicon(transliteration_text):\n INPUT_DIR = \"/kaggle/input/deep-past-initiative-machine-translation\"\n df = pd.read_csv(f\"{INPUT_DIR}/OA_Lexicon_eBL.csv\")\n \n subset = df.loc[df['type'] == 'PN', ['form', 'norm']].dropna().drop_duplicates()\n names = subset.values.tolist()\n names.sort(key=lambda x: len(str(x[0])), reverse=True)\n \n name_map = {}\n processed_text = transliteration_text\n \n counter = 0\n for form, norm in names:\n form_str = str(form)\n if len(form_str) <= 2:\n continue\n \n pattern = re.compile(rf'(?= 20: \n break\n \n return processed_text, name_map\n\ndef restore_names(translated_text, name_map):\n restored_text = translated_text\n for placeholder, original_name in name_map.items():\n restored_text = restored_text.replace(placeholder, original_name)\n return restored_text\n\n# End of own code","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-26T14:35:12.907715Z","iopub.execute_input":"2026-03-26T14:35:12.907979Z","iopub.status.idle":"2026-03-26T14:35:12.921258Z","shell.execute_reply.started":"2026-03-26T14:35:12.907954Z","shell.execute_reply":"2026-03-26T14:35:12.920574Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Fix the seed (for reproducibility).\ndef seed_everything(seed=42):\n torch.manual_seed(seed)\n np.random.seed(seed)\n torch.cuda.manual_seed(seed)\n \nseed_everything()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-26T14:35:12.922250Z","iopub.execute_input":"2026-03-26T14:35:12.922878Z","iopub.status.idle":"2026-03-26T14:35:12.939740Z","shell.execute_reply.started":"2026-03-26T14:35:12.922854Z","shell.execute_reply":"2026-03-26T14:35:12.938989Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\ndef preprocess_input_text(transliteration_text):\n \"\"\"Optimized with pre-compiled patterns\"\"\"\n if pd.isna(transliteration_text):\n return \"\"\n \n text = str(transliteration_text)\n text = PATTERNS.BIG_GAP_INPUT.sub('', text)\n text = PATTERNS.SMALL_GAP_INPUT.sub('', text)\n text = implement_lexicon(transliteration_text) #should have been text, name_map and name_map should have been stored such that it could \n # have been called in postprocessing, and might have been better to call earlier\n return text\n\n# ============================================\n# OPTIMIZED POSTPROCESSING (~5x faster)\n# ============================================\n\n# Pre-create translation tables (created once)\nSUBSCRIPT_TABLE = str.maketrans(\"₀₁₂₃₄₅₆₇₈₉\", \"0123456789\")\nFORBIDDEN_CHARS_TABLE = str.maketrans('', '', '!?()\"—Loss–<>⌈⌋⌊[]+ʾ/;')\n\ndef postprocess_translation_output(translation_text):\n \"\"\"\n Optimized postprocessing with pre-compiled patterns\n ~5x faster than original due to regex pre-compilation\n \"\"\"\n if not isinstance(translation_text, str) or not translation_text.strip():\n return \"\"\n \n text = translation_text\n \n # Step 1: Normalize Akkadian characters\n text = text.replace('ḫ', 'h').replace('Ḫ', 'H')\n \n # Step 2: Subscript conversion (pre-created table)\n text = text.translate(SUBSCRIPT_TABLE)\n \n # Step 3-4: Gap normalization (pre-compiled)\n text = PATTERNS.AKKADIAN_GAP.sub('', text)\n text = PATTERNS.BIG_GAP_OUTPUT.sub('', text)\n text = PATTERNS.MERGE_GAPS.sub(' ', text)\n text = PATTERNS.MERGE_BIG_GAPS.sub(' ', text)\n \n # Step 5: Remove grammar annotations\n text = PATTERNS.GRAMMAR_ANNOTATIONS.sub('', text)\n \n # Step 6-8: Protect gaps, remove forbidden, restore\n text = text.replace('', '\\x00GAP\\x00').replace('', '\\x00BIG\\x00')\n text = text.translate(FORBIDDEN_CHARS_TABLE)\n text = text.replace('\\x00GAP\\x00', ' ').replace('\\x00BIG\\x00', ' ')\n # Restore Names should have been called here\n \n # Step 9: Fraction conversion (pre-compiled patterns)\n for pattern, replacement in PATTERNS.FRACTION_PATTERNS:\n text = pattern.sub(replacement, text)\n \n # Step 10: Remove repeated words\n text = PATTERNS.REPEATED_WORDS.sub(r'\\1', text)\n \n # Step 11: Remove repeated n-grams (pre-compiled)\n for ngram_pattern in PATTERNS.NGRAM_PATTERNS:\n text = ngram_pattern.sub(r'\\1', text)\n \n # Step 12-13: Final cleanup\n text = PATTERNS.SPACE_BEFORE_PUNCT.sub(r'\\1', text)\n text = PATTERNS.REPEATED_PUNCT.sub(r'\\1', text)\n text = PATTERNS.MULTI_SPACE.sub(' ', text).strip().strip('-').strip()\n \n return text","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-26T14:35:12.941688Z","iopub.execute_input":"2026-03-26T14:35:12.941963Z","iopub.status.idle":"2026-03-26T14:35:12.950793Z","shell.execute_reply.started":"2026-03-26T14:35:12.941940Z","shell.execute_reply":"2026-03-26T14:35:12.950043Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"INPUT_DIR = \"/kaggle/input/deep-past-initiative-machine-translation\"\ntrain_df = pd.read_csv(f\"{INPUT_DIR}/train.csv\")\ntest_df = pd.read_csv(f\"{INPUT_DIR}/test.csv\")","metadata":{"execution":{"iopub.status.busy":"2026-03-26T14:35:12.951811Z","iopub.execute_input":"2026-03-26T14:35:12.952056Z","iopub.status.idle":"2026-03-26T14:35:13.019654Z","shell.execute_reply.started":"2026-03-26T14:35:12.952035Z","shell.execute_reply":"2026-03-26T14:35:13.018811Z"},"papermill":{"duration":0.064163,"end_time":"2025-12-18T06:39:34.891536","exception":false,"start_time":"2025-12-18T06:39:34.827373","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f\"Original Train Data: {len(train_df)} docs\")","metadata":{"execution":{"iopub.status.busy":"2026-03-26T14:35:13.020790Z","iopub.execute_input":"2026-03-26T14:35:13.021152Z","iopub.status.idle":"2026-03-26T14:35:13.025359Z","shell.execute_reply.started":"2026-03-26T14:35:13.021114Z","shell.execute_reply":"2026-03-26T14:35:13.024590Z"},"papermill":{"duration":0.008111,"end_time":"2025-12-18T06:39:34.901991","exception":false,"start_time":"2025-12-18T06:39:34.89388","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def simple_sentence_aligner(df):\n \"\"\"\n 【戦略の肝】\n Trainデータの「文書(複数文)」を、Testデータと同じ「文(1文)」に分割します。\n ここでは「英語の文数」と「アッカド語の行数」が一致する場合のみ分割する\n というヒューリスティック（簡易ルール）を使います。\n \"\"\"\n aligned_data = []\n \n for idx, row in df.iterrows():\n src = str(row['transliteration'])\n tgt = str(row['translation'])\n \n # Split the English text by sentence-ending punctuation.\n tgt_sents = [t.strip() for t in re.split(r'(?<=[.!?])\\s+', tgt) if t.strip()]\n \n # Assume the Akkadian text is often separated by newlines and split accordingly.\n src_lines = [s.strip() for s in src.split('\\n') if s.strip()]\n \n # If the counts match, trust it as 1-to-1 pairs and use the split version.\n if len(tgt_sents) > 1 and len(tgt_sents) == len(src_lines):\n for s, t in zip(src_lines, tgt_sents):\n if len(s) > 3 and len(t) > 3: # Remove junk/noisy data.\n aligned_data.append({'transliteration': s, 'translation': t})\n else:\n # If splitting fails (counts don't match), keep the original document pair as-is (safe fallback).\n aligned_data.append({'transliteration': src, 'translation': tgt})\n \n return pd.DataFrame(aligned_data)\n","metadata":{"execution":{"iopub.status.busy":"2026-03-26T14:35:13.026382Z","iopub.execute_input":"2026-03-26T14:35:13.026698Z","iopub.status.idle":"2026-03-26T14:35:13.039652Z","shell.execute_reply.started":"2026-03-26T14:35:13.026677Z","shell.execute_reply":"2026-03-26T14:35:13.038984Z"},"papermill":{"duration":0.010612,"end_time":"2025-12-18T06:39:34.914968","exception":false,"start_time":"2025-12-18T06:39:34.904356","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Run data augmentation.\ntrain_expanded = simple_sentence_aligner(train_df)\nprint(f\"Expanded Train Data: {len(train_expanded)} sentences (Alignment applied)\")\n\n# Convert to Hugging Face Dataset format & split into Train/Val.\ndataset = Dataset.from_pandas(train_expanded)\n# Create a validation set with test_size=0.1.\nsplit_datasets = dataset.train_test_split(test_size=0.1, seed=42)\n# After splitting, the keys are 'train' and 'test' (we'll use 'test' as validation).\n","metadata":{"execution":{"iopub.status.busy":"2026-03-26T14:35:13.040568Z","iopub.execute_input":"2026-03-26T14:35:13.040805Z","iopub.status.idle":"2026-03-26T14:35:13.207358Z","shell.execute_reply.started":"2026-03-26T14:35:13.040783Z","shell.execute_reply":"2026-03-26T14:35:13.206643Z"},"papermill":{"duration":0.13294,"end_time":"2025-12-18T06:39:35.050272","exception":false,"start_time":"2025-12-18T06:39:34.917332","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ==========================================\n# 3. Tokenization & preprocessing\n# ==========================================\ntokenizer = AutoTokenizer.from_pretrained(Config.MODEL_NAME)\n\n# Own Code\nname_placeholders = [f\"PN_{i}\" for i in range(20)]\n\nspecial_tokens_dict = {'additional_special_tokens': name_placeholders}\nnum_added_toks = tokenizer.add_special_tokens(special_tokens_dict)\n# End of own code\n\n# Fix the corresponding section in dpc-starter-train.\nPREFIX = \"translate Akkadian to English: \"\n\ndef preprocess_function(examples):\n inputs = [PREFIX + str(ex) for ex in examples[\"transliteration\"]]\n targets = [str(ex) for ex in examples[\"translation\"]]\n \n model_inputs = tokenizer(inputs, max_length=Config.MAX_LENGTH, truncation=True)\n labels = tokenizer(targets, max_length=Config.MAX_LENGTH, truncation=True)\n \n model_inputs[\"labels\"] = labels[\"input_ids\"]\n return model_inputs\n\n #own code:\n masked_src, n_map = implement_lexicon(translit_text)\n masked_tgt = translation_text\n\n for placeholder, original in n_map.items():\n masked_tgt = masked_tgt.replace(original, placeholder)\n\n#end of own code\nreduced_train = split_datasets[\"train\"].shuffle(seed=42).select(range(200))\nreduced_val = split_datasets[\"test\"].shuffle(seed=42).select(range(50))\n\n\ntokenized_train = reduced_train.map(preprocess_function, batched=True)\ntokenized_val = reduced_val.map(preprocess_function, batched=True)\n","metadata":{"execution":{"iopub.status.busy":"2026-03-26T14:35:13.208372Z","iopub.execute_input":"2026-03-26T14:35:13.208779Z","iopub.status.idle":"2026-03-26T14:35:14.400438Z","shell.execute_reply.started":"2026-03-26T14:35:13.208744Z","shell.execute_reply":"2026-03-26T14:35:14.399576Z"},"papermill":{"duration":3.481316,"end_time":"2025-12-18T06:39:38.534136","exception":false,"start_time":"2025-12-18T06:39:35.05282","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ==========================================\n# 4. Model training (fine-tuning)\n# ==========================================\ngc.collect()\ntorch.cuda.empty_cache()\nmodel = AutoModelForSeq2SeqLM.from_pretrained(Config.MODEL_NAME)\n# Own code\nmodel.resize_token_embeddings(len(tokenizer))\n# End of own code\ndata_collator = DataCollatorForSeq2Seq(tokenizer, model=model)\n\n# Metric (chrF++ is part of the competition metric and measures character-level precision/overlap).\nmetric = evaluate.load(\"chrf\")\n\ndef compute_metrics(eval_preds):\n preds, labels = eval_preds\n if isinstance(preds, tuple): preds = preds[0]\n \n decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n # Ignore -100 in the labels.\n labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n \n decoded_preds = [pred.strip() for pred in decoded_preds]\n decoded_labels = [[label.strip()] for label in decoded_labels]\n \n result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n return {\"chrf\": result[\"score\"]}\n\nargs = Seq2SeqTrainingArguments(\n output_dir=Config.OUTPUT_DIR,\n eval_strategy=\"epoch\",\n save_strategy=\"epoch\",\n learning_rate=Config.LEARNING_RATE,\n \n # === Key fixes ===\n fp16=False, # ★Set to False to prevent a NaN error (required).\n per_device_train_batch_size=4, # ★fp32 uses more memory, so reduce the batch size (8 -> 4).\n per_device_eval_batch_size=4,\n gradient_accumulation_steps=2, # ★To compensate, accumulate gradients to keep the effective batch size at 8.\n # ======================\n \n weight_decay=0.01,\n save_total_limit=2,\n num_train_epochs=Config.EPOCHS,\n predict_with_generate=True,\n logging_steps=10, # Inspect logs in more detail.\n report_to=\"none\"\n)\n\ntrainer = Seq2SeqTrainer(\n model=model,\n args=args,\n train_dataset=tokenized_train,\n eval_dataset=tokenized_val,\n data_collator=data_collator,\n tokenizer=tokenizer,\n compute_metrics=compute_metrics\n)\n\nprint(\"Starting Training (FP32 mode)...\")\ntrainer.train()\n","metadata":{"execution":{"iopub.status.busy":"2026-03-26T14:35:14.401543Z","iopub.execute_input":"2026-03-26T14:35:14.401822Z","iopub.status.idle":"2026-03-26T14:50:14.196180Z","shell.execute_reply.started":"2026-03-26T14:35:14.401796Z","shell.execute_reply":"2026-03-26T14:50:14.195314Z"},"papermill":{"duration":3815.090022,"end_time":"2025-12-18T07:43:13.627558","exception":false,"start_time":"2025-12-18T06:39:38.537536","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# --- Save Model ---\n# Important: the model saved here will be loaded in the next notebook.\ntrainer.save_model(Config.OUTPUT_DIR)\ntokenizer.save_pretrained(Config.OUTPUT_DIR)\nprint(f\"Model saved to {Config.OUTPUT_DIR}\")\n","metadata":{"execution":{"iopub.status.busy":"2026-03-26T14:50:14.197460Z","iopub.execute_input":"2026-03-26T14:50:14.197877Z","iopub.status.idle":"2026-03-26T14:50:16.537267Z","shell.execute_reply.started":"2026-03-26T14:50:14.197848Z","shell.execute_reply":"2026-03-26T14:50:16.536222Z"},"papermill":{"duration":8.315075,"end_time":"2025-12-18T07:43:21.974867","exception":false,"start_time":"2025-12-18T07:43:13.659792","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null}]}