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Deep Past Initiative – Machine Translation (Training Notebook)\n\nThis notebook is a **starter / baseline** for this Kaggle competition.\n\nMain ideas:\n- Use **ByT5** to handle noisy Akkadian transliterations at the character level\n- Perform **simple sentence alignment** to increase training data\n- Fine-tune using HuggingFace `Trainer`\n\n\nInference Code is [here](https://www.kaggle.com/code/takamichitoda/dpc-starter-infer).","metadata":{}},{"cell_type":"code","source":"!pip install evaluate sacrebleu","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2025-12-25T04:39:41.352260Z","iopub.execute_input":"2025-12-25T04:39:41.352510Z","iopub.status.idle":"2025-12-25T04:39:46.524502Z","shell.execute_reply.started":"2025-12-25T04:39:41.352483Z","shell.execute_reply":"2025-12-25T04:39:46.523645Z"},"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","metadata":{"execution":{"iopub.status.busy":"2025-12-25T04:39:46.526671Z","iopub.execute_input":"2025-12-25T04:39:46.526927Z","iopub.status.idle":"2025-12-25T04:40:16.181918Z","shell.execute_reply.started":"2025-12-25T04:39:46.526900Z","shell.execute_reply":"2025-12-25T04:40:16.181307Z"},"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    EPOCHS = 20\n    LEARNING_RATE = 1e-4\n    OUTPUT_DIR = \"./byt5-akkadian-model\"","metadata":{"execution":{"iopub.status.busy":"2025-12-25T04:40:16.182812Z","iopub.execute_input":"2025-12-25T04:40:16.183782Z","iopub.status.idle":"2025-12-25T04:40:16.187481Z","shell.execute_reply.started":"2025-12-25T04:40:16.183754Z","shell.execute_reply":"2025-12-25T04:40:16.186704Z"},"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":"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":{"execution":{"iopub.status.busy":"2025-12-25T04:40:16.188644Z","iopub.execute_input":"2025-12-25T04:40:16.188917Z","iopub.status.idle":"2025-12-25T04:40:16.228219Z","shell.execute_reply.started":"2025-12-25T04:40:16.188884Z","shell.execute_reply":"2025-12-25T04:40:16.227627Z"},"papermill":{"duration":0.011227,"end_time":"2025-12-18T06:39:34.825004","exception":false,"start_time":"2025-12-18T06:39:34.813777","status":"completed"},"tags":[],"trusted":true},"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":"2025-12-25T04:40:16.229204Z","iopub.execute_input":"2025-12-25T04:40:16.229514Z","iopub.status.idle":"2025-12-25T04:40:16.293027Z","shell.execute_reply.started":"2025-12-25T04:40:16.229485Z","shell.execute_reply":"2025-12-25T04:40:16.292488Z"},"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":"2025-12-25T04:40:16.293782Z","iopub.execute_input":"2025-12-25T04:40:16.294016Z","iopub.status.idle":"2025-12-25T04:40:16.298403Z","shell.execute_reply.started":"2025-12-25T04:40:16.293994Z","shell.execute_reply":"2025-12-25T04:40:16.297717Z"},"papermill":{"duration":0.008111,"end_time":"2025-12-18T06:39:34.901991","exception":false,"start_time":"2025-12-18T06:39:34.893880","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)","metadata":{"execution":{"iopub.status.busy":"2025-12-25T04:40:16.300085Z","iopub.execute_input":"2025-12-25T04:40:16.300585Z","iopub.status.idle":"2025-12-25T04:40:16.311985Z","shell.execute_reply.started":"2025-12-25T04:40:16.300548Z","shell.execute_reply":"2025-12-25T04:40:16.311356Z"},"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).","metadata":{"execution":{"iopub.status.busy":"2025-12-25T04:40:16.312748Z","iopub.execute_input":"2025-12-25T04:40:16.312993Z","iopub.status.idle":"2025-12-25T04:40:16.451129Z","shell.execute_reply.started":"2025-12-25T04:40:16.312972Z","shell.execute_reply":"2025-12-25T04:40:16.450510Z"},"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":"def create_bidirectional_data(dataset_split):\n    df = dataset_split.to_pandas()\n    \n    # 方向1: Akkadian -> English (元のタスク)\n    df_fwd = df.copy()\n    df_fwd['input_text'] = \"translate Akkadian to English: \" + df_fwd['transliteration'].astype(str)\n    df_fwd['target_text'] = df_fwd['translation'].astype(str)\n    \n    # 方向2: English -> Akkadian (逆翻訳タスク)\n    df_bwd = df.copy()\n    df_bwd['input_text'] = \"translate English to Akkadian: \" + df_bwd['translation'].astype(str)\n    df_bwd['target_text'] = df_bwd['transliteration'].astype(str)\n    \n    # 結合してシャッフル\n    df_combined = pd.concat([df_fwd, df_bwd], ignore_index=True)\n    df_combined = df_combined.sample(frac=1, random_state=42).reset_index(drop=True)\n    \n    return Dataset.from_pandas(df_combined)\n\ndef create_unidirectional_data(dataset_split):\n    # Validation用: 形式だけ揃える (Akkadian -> Englishのみ)\n    df = dataset_split.to_pandas()\n    df['input_text'] = \"translate Akkadian to English: \" + df['transliteration'].astype(str)\n    df['target_text'] = df['translation'].astype(str)\n    return Dataset.from_pandas(df)\n\n# Trainデータは双方向化 (データ数が2倍になります)\nbidirectional_train = create_bidirectional_data(split_datasets[\"train\"])\n\n# Validationデータは単方向のまま (評価のため)\nunidirectional_val = create_unidirectional_data(split_datasets[\"test\"])\n\nprint(f\"Train samples: {len(bidirectional_train)} (Bidirectional)\")\nprint(f\"Val samples:   {len(unidirectional_val)} (Unidirectional)\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-25T04:40:16.451989Z","iopub.execute_input":"2025-12-25T04:40:16.452256Z","iopub.status.idle":"2025-12-25T04:40:16.544430Z","shell.execute_reply.started":"2025-12-25T04:40:16.452231Z","shell.execute_reply":"2025-12-25T04:40:16.543742Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ==========================================\n# 3. Tokenization & preprocessing\n# ==========================================\ntokenizer = AutoTokenizer.from_pretrained(Config.MODEL_NAME)\n\ndef preprocess_function(examples):\n    # データ作成時にPrefix込みの \"input_text\" と \"target_text\" を作っているのでそれを使う\n    inputs = [str(ex) for ex in examples[\"input_text\"]]\n    targets = [str(ex) for ex in examples[\"target_text\"]]\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# 作成した新しいデータセットに対してmapを適用\ntokenized_train = bidirectional_train.map(preprocess_function, batched=True)\ntokenized_val = unidirectional_val.map(preprocess_function, batched=True)","metadata":{"execution":{"iopub.status.busy":"2025-12-25T04:41:45.786304Z","iopub.execute_input":"2025-12-25T04:41:45.786864Z","iopub.status.idle":"2025-12-25T04:41:49.539833Z","shell.execute_reply.started":"2025-12-25T04:41:45.786835Z","shell.execute_reply":"2025-12-25T04:41:49.539238Z"},"papermill":{"duration":3.481316,"end_time":"2025-12-18T06:39:38.534136","exception":false,"start_time":"2025-12-18T06:39:35.052820","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)\ndata_collator = DataCollatorForSeq2Seq(tokenizer, model=model)\n\n# 評価指標の準備\nmetric_chrf = evaluate.load(\"chrf\")\nmetric_bleu = evaluate.load(\"sacrebleu\")\n\ndef compute_metrics(eval_preds):\n    preds, labels = eval_preds\n\n    # Seq2SeqTrainer/Trainer の返り値が tuple になることがあるので保険\n    if isinstance(preds, tuple):\n        preds = preds[0]\n\n    # ★ここが重要★\n    # preds が logits の場合: (batch, seq, vocab) になりがち → argmax で token ids に変換\n    if hasattr(preds, \"ndim\") and preds.ndim == 3:\n        preds = np.argmax(preds, axis=-1)\n\n    # int 化 & 範囲外対策（ByT5 の decode が chr を使うため）\n    preds = preds.astype(np.int64)\n    preds = np.where(preds < 0, tokenizer.pad_token_id, preds)\n    preds = np.clip(preds, 0, tokenizer.vocab_size - 1)\n\n    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n\n    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n\n    # 既存の評価計算（あなたの続きの処理に合わせて）\n    chrf = metric_chrf.compute(predictions=decoded_preds, references=decoded_labels)[\"score\"]\n    bleu = metric_bleu.compute(predictions=decoded_preds, references=[[x] for x in decoded_labels])[\"score\"]\n\n    geo_mean = (chrf * bleu) ** 0.5 if chrf > 0 and bleu > 0 else 0.0\n    return {\"chrf\": chrf, \"bleu\": bleu, \"geo_mean\": geo_mean}\n\n\nargs = Seq2SeqTrainingArguments(\n    output_dir=Config.OUTPUT_DIR,\n    eval_strategy=\"epoch\",\n    save_strategy=\"epoch\",\n    learning_rate=Config.LEARNING_RATE,\n    optim=\"adafactor\",\n    label_smoothing_factor=0.2,\n    \n    # === Key fixes ===\n    fp16=False,                     # ★Set to False to prevent a NaN error (required).\n    per_device_train_batch_size=1,  # ★fp32 uses more memory, so reduce the batch size (8 -> 4).\n    per_device_eval_batch_size=1,\n    gradient_accumulation_steps=8,  # ★To compensate, accumulate gradients to keep the effective batch size at 8.\n    # ======================\n    \n    weight_decay=0.01,\n    save_total_limit=1,\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    #load_best_model_at_end=True,\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()","metadata":{"execution":{"iopub.status.busy":"2025-12-25T04:42:07.928093Z","iopub.execute_input":"2025-12-25T04:42:07.928859Z","execution_failed":"2025-12-25T04:42:30.395Z"},"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}\")","metadata":{"execution":{"execution_failed":"2025-12-25T04:23:11.994Z"},"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},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}