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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\nOriginal train  : https://www.kaggle.com/code/takamichitoda/dpc-starter-train, fixed metric calculation, adding API for uploading model as kaggle dataset\n\nBest checkpoint : https://www.kaggle.com/datasets/llkh0a/byt5-akkadian-model is resume train from \n\nthis checkpoint : https://www.kaggle.com/datasets/jeanjean111/byt5-base-big-data2","metadata":{"papermill":{"duration":0.011429,"end_time":"2025-12-31T11:12:27.680792","exception":false,"start_time":"2025-12-31T11:12:27.669363","status":"completed"},"tags":[]}},{"id":"8cd99f5a","cell_type":"markdown","source":"# Configuration","metadata":{}},{"id":"83fa2710","cell_type":"code","source":"!pip install evaluate sacrebleu","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2025-12-31T11:12:27.701858Z","iopub.status.busy":"2025-12-31T11:12:27.701569Z","iopub.status.idle":"2025-12-31T11:12:32.564837Z","shell.execute_reply":"2025-12-31T11:12:32.564031Z"},"papermill":{"duration":4.875828,"end_time":"2025-12-31T11:12:32.566563","exception":false,"start_time":"2025-12-31T11:12:27.690735","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"9816668f","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.execute_input":"2025-12-31T11:12:32.588991Z","iopub.status.busy":"2025-12-31T11:12:32.588755Z","iopub.status.idle":"2025-12-31T11:13:04.418570Z","shell.execute_reply":"2025-12-31T11:13:04.417950Z"},"papermill":{"duration":31.842871,"end_time":"2025-12-31T11:13:04.420243","exception":false,"start_time":"2025-12-31T11:12:32.577372","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"e8c664cc","cell_type":"code","source":"class Config:\n    \"\"\"Configuration for ByT5 Akkadian-to-English Machine Translation\"\"\"\n    # ====== Model Configuration ======\n    MODEL_NAME = \"google/byt5-base\"\n    PRETRAINED_DIR = None  # Set to a path to resume training; None to start from scratch\n    PRETRAINED_DIR = \"/kaggle/input/byt5-akkadian-model\"\n    MAX_LENGTH = 512\n    \n    # ====== Training Configuration ======\n    INPUT_DIR = \"/kaggle/input/deep-past-initiative-machine-translation\"\n    SEED = 42\n    EPOCHS = 1\n    LEARNING_RATE = 1e-4\n    TRAIN_BATCH_SIZE = 2    # per_device_train_batch_size\n    EVAL_BATCH_SIZE = 2     # per_device_eval_batch_size\n    GRADIENT_ACCUMULATION_STEPS = 8\n    OUTPUT_DIR = \"./byt5-akkadian-model\"\n    \n    # ====== Inference & Upload Configuration ======\n    MAKE_SUBMISSION = False   # Set to True to run inference on test set\n    UPLOAD_MODEL = False     # Set to True to upload model to Kaggle\n    KAGGLE_USERNAME = \"<redacted>\"\n    KAGGLE_KEY = \"<redacted>\"\n    DATASET_NAME = \"byt5-akkadian-model-private\"","metadata":{"execution":{"iopub.execute_input":"2025-12-31T11:13:04.444218Z","iopub.status.busy":"2025-12-31T11:13:04.443685Z","iopub.status.idle":"2025-12-31T11:13:04.447867Z","shell.execute_reply":"2025-12-31T11:13:04.447079Z"},"papermill":{"duration":0.016977,"end_time":"2025-12-31T11:13:04.449226","exception":false,"start_time":"2025-12-31T11:13:04.432249","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"813f2c22","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(Config.SEED)","metadata":{"execution":{"iopub.execute_input":"2025-12-31T11:13:04.471341Z","iopub.status.busy":"2025-12-31T11:13:04.471065Z","iopub.status.idle":"2025-12-31T11:13:04.478164Z","shell.execute_reply":"2025-12-31T11:13:04.477646Z"},"papermill":{"duration":0.019909,"end_time":"2025-12-31T11:13:04.479617","exception":false,"start_time":"2025-12-31T11:13:04.459708","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"ba3c7944","cell_type":"code","source":"INPUT_DIR = Config.INPUT_DIR\ntrain_df = pd.read_csv(f\"{INPUT_DIR}/train.csv\")\ntest_df = pd.read_csv(f\"{INPUT_DIR}/test.csv\")","metadata":{"execution":{"iopub.execute_input":"2025-12-31T11:13:04.501839Z","iopub.status.busy":"2025-12-31T11:13:04.501242Z","iopub.status.idle":"2025-12-31T11:13:04.552973Z","shell.execute_reply":"2025-12-31T11:13:04.552377Z"},"papermill":{"duration":0.064375,"end_time":"2025-12-31T11:13:04.554492","exception":false,"start_time":"2025-12-31T11:13:04.490117","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"cefefbaa","cell_type":"code","source":"print(f\"Original Train Data: {len(train_df)} docs\")","metadata":{"execution":{"iopub.execute_input":"2025-12-31T11:13:04.576521Z","iopub.status.busy":"2025-12-31T11:13:04.576250Z","iopub.status.idle":"2025-12-31T11:13:04.579866Z","shell.execute_reply":"2025-12-31T11:13:04.579237Z"},"papermill":{"duration":0.016041,"end_time":"2025-12-31T11:13:04.581246","exception":false,"start_time":"2025-12-31T11:13:04.565205","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"3eb845ea","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.execute_input":"2025-12-31T11:13:04.603831Z","iopub.status.busy":"2025-12-31T11:13:04.603361Z","iopub.status.idle":"2025-12-31T11:13:04.610708Z","shell.execute_reply":"2025-12-31T11:13:04.610079Z"},"papermill":{"duration":0.020125,"end_time":"2025-12-31T11:13:04.612093","exception":false,"start_time":"2025-12-31T11:13:04.591968","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"fc3b110e","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.execute_input":"2025-12-31T11:13:04.633986Z","iopub.status.busy":"2025-12-31T11:13:04.633554Z","iopub.status.idle":"2025-12-31T11:13:04.756687Z","shell.execute_reply":"2025-12-31T11:13:04.755967Z"},"papermill":{"duration":0.135706,"end_time":"2025-12-31T11:13:04.758219","exception":false,"start_time":"2025-12-31T11:13:04.622513","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"593731ad","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":{"execution":{"iopub.execute_input":"2025-12-31T11:13:04.781010Z","iopub.status.busy":"2025-12-31T11:13:04.780547Z","iopub.status.idle":"2025-12-31T11:13:04.866554Z","shell.execute_reply":"2025-12-31T11:13:04.865681Z"},"papermill":{"duration":0.098976,"end_time":"2025-12-31T11:13:04.868008","exception":false,"start_time":"2025-12-31T11:13:04.769032","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"b629c7b3","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.execute_input":"2025-12-31T11:13:04.893410Z","iopub.status.busy":"2025-12-31T11:13:04.892838Z","iopub.status.idle":"2025-12-31T11:13:09.072779Z","shell.execute_reply":"2025-12-31T11:13:09.072134Z"},"papermill":{"duration":4.194828,"end_time":"2025-12-31T11:13:09.074221","exception":false,"start_time":"2025-12-31T11:13:04.879393","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"8de8004e","cell_type":"code","source":"from transformers import TrainerCallback\n\nDEBUG_EVAL_PRINTED = True\n\nclass PrintMetricsCallback(TrainerCallback):\n    def on_evaluate(self, args, state, control, metrics=None, **kwargs):\n        epoch = getattr(state, \"epoch\", None)\n        # find the most recent training loss from the log history\n        train_loss = None\n        for entry in reversed(state.log_history):\n            if \"loss\" in entry:\n                train_loss = entry[\"loss\"]\n                break\n        prefix = f\"[Epoch {epoch}] \" if epoch is not None else \"\"\n        # if train_loss is not None:\n        #     print(prefix + f\"Train Loss={train_loss:.4f}\")\n        if metrics:\n            keys = [\"eval_loss\", \"eval_chrf\", \"eval_bleu\", \"eval_geo_mean\"]\n            metric_items = \", \".join(f\"{k}={metrics[k]:.4f}\" for k in keys if k in metrics)\n            print(prefix + f\"Train Loss={train_loss:.4f}, \"+metric_items)","metadata":{},"outputs":[],"execution_count":null},{"id":"3307b2ac","cell_type":"markdown","source":"# Load Pretrained Model (Optional)","metadata":{}},{"id":"e96f25c4","cell_type":"code","source":"# ==========================================\n# 4. Model training (fine-tuning)\n# ==========================================\ngc.collect()\ntorch.cuda.empty_cache()\n","metadata":{"execution":{"iopub.execute_input":"2025-12-31T11:13:09.097964Z","iopub.status.busy":"2025-12-31T11:13:09.097539Z","iopub.status.idle":"2025-12-31T13:49:47.591491Z","shell.execute_reply":"2025-12-31T13:49:47.590819Z"},"papermill":{"duration":9398.507298,"end_time":"2025-12-31T13:49:47.593119","exception":false,"start_time":"2025-12-31T11:13:09.085821","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"d3e6f93a","cell_type":"code","source":"gc.collect()\ntorch.cuda.empty_cache()\n\n# Load model based on PRETRAINED_DIR\nif Config.PRETRAINED_DIR is not None:\n    print(f\"Loading pretrained model from {Config.PRETRAINED_DIR}...\")\n    tokenizer = AutoTokenizer.from_pretrained(\n        Config.PRETRAINED_DIR,\n        local_files_only=True,\n        use_fast=False,  # byt5 uses a byte-level tokenizer; safe to set use_fast=False\n    )\n    model = AutoModelForSeq2SeqLM.from_pretrained(\n        Config.PRETRAINED_DIR,\n        local_files_only=True,\n    )\n    print(\"✓ Pretrained model loaded successfully\")\nelse:\n    print(f\"Training from scratch using {Config.MODEL_NAME}...\")\n    tokenizer = AutoTokenizer.from_pretrained(Config.MODEL_NAME)\n    model = AutoModelForSeq2SeqLM.from_pretrained(Config.MODEL_NAME)\n    print(\"✓ Base model loaded successfully\")","metadata":{},"outputs":[],"execution_count":null},{"id":"e38f126f","cell_type":"code","source":"data_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    # === Fix: Replace -100 in labels before decoding ===\n    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n    # ===================================================\n\n    # Wrap references in list of lists for both metrics to be safe\n    formatted_refs = [[x] for x in decoded_labels]\n\n    chrf = metric_chrf.compute(predictions=decoded_preds, references=formatted_refs)[\"score\"]\n    bleu = metric_bleu.compute(predictions=decoded_preds, references=formatted_refs)[\"score\"]\n    geo_mean = (chrf * bleu) ** 0.5 if chrf > 0 and bleu > 0 else 0.0\n    \n    return {\"chrf\": chrf, \"bleu\": bleu, \"geo_mean\": geo_mean}\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=Config.TRAIN_BATCH_SIZE,\n    per_device_eval_batch_size=Config.EVAL_BATCH_SIZE,\n    gradient_accumulation_steps=Config.GRADIENT_ACCUMULATION_STEPS,\n    # ======================\n    generation_max_length=Config.MAX_LENGTH,  # <--- Add this (512). Crucial for ByT5.\n    generation_num_beams=2, \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    # show/use competition_metric as the main criterion\n    load_best_model_at_end=True,\n    metric_for_best_model=\"geo_mean\",\n    greater_is_better=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    callbacks=[PrintMetricsCallback()]\n)\n\nprint(\"Starting Training (FP32 mode)...\")\ntrainer.train()","metadata":{},"outputs":[],"execution_count":null},{"id":"00214f74","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":{"iopub.execute_input":"2025-12-31T13:49:47.618305Z","iopub.status.busy":"2025-12-31T13:49:47.617928Z","iopub.status.idle":"2025-12-31T13:49:49.411037Z","shell.execute_reply":"2025-12-31T13:49:49.410325Z"},"papermill":{"duration":1.807291,"end_time":"2025-12-31T13:49:49.412677","exception":false,"start_time":"2025-12-31T13:49:47.605386","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"e18f67c7","cell_type":"markdown","source":"# Upload Model via Kaggle API","metadata":{}},{"id":"566fef14","cell_type":"code","source":"# === Upload best model to Kaggle Dataset (Optional) ===\n# Only upload if UPLOAD_MODEL is True\n\nif not Config.UPLOAD_MODEL:\n    print(\"Model upload disabled (Config.UPLOAD_MODEL = False)\")\nelse:\n    import json\n    import os\n    import subprocess\n    from pathlib import Path\n    import getpass\n    from datetime import datetime\n    \n    # 1. Setup Credentials\n    kaggle_api_token = {\n        \"username\": Config.KAGGLE_USERNAME, \n        \"key\": Config.KAGGLE_KEY\n    }\n    \n    # Write to default location ~/.kaggle/kaggle.json\n    p = Path.home() / \".kaggle\"\n    p.mkdir(exist_ok=True)\n    k = p / \"kaggle.json\"\n    k.write_text(json.dumps(kaggle_api_token))\n    try:\n        k.chmod(0o600)\n    except Exception:\n        pass  # chmod may not work on Windows\n    \n    # Also set env vars for the current process\n    os.environ[\"KAGGLE_USERNAME\"] = kaggle_api_token[\"username\"]\n    os.environ[\"KAGGLE_KEY\"] = kaggle_api_token[\"key\"]\n\n    def ensure_kaggle_creds(interactive=True):\n        \"\"\"Ensure Kaggle credentials exist. If not and interactive==True, prompt the user to enter them.\"\"\"\n        kaggle_json = Path.home() / \".kaggle\" / \"kaggle.json\"\n        if kaggle_json.exists():\n            try:\n                with open(kaggle_json, \"r\") as f:\n                    data = json.load(f)\n                os.environ[\"KAGGLE_USERNAME\"] = data.get(\"username\", \"\")\n                os.environ[\"KAGGLE_KEY\"] = data.get(\"key\", \"\")\n                return True\n            except Exception as e:\n                print(\"Failed to read existing ~/.kaggle/kaggle.json:\", e)\n\n        if not interactive:\n            print(\"No ~/.kaggle/kaggle.json found and interactive mode is off.\")\n            return False\n\n        print(\"No ~/.kaggle/kaggle.json found. Please enter Kaggle credentials to create it.\")\n        username = input(\"Kaggle username: \").strip()\n        key = getpass.getpass(\"Kaggle API key (hidden): \").strip()\n\n        if username and key:\n            kaggle_json.parent.mkdir(parents=True, exist_ok=True)\n            with open(kaggle_json, \"w\") as f:\n                json.dump({\"username\": username, \"key\": key}, f)\n            try:\n                kaggle_json.chmod(0o600)\n            except Exception:\n                pass\n            os.environ[\"KAGGLE_USERNAME\"] = username\n            os.environ[\"KAGGLE_KEY\"] = key\n            print(\"Credentials saved to ~/.kaggle/kaggle.json\")\n            return True\n        else:\n            print(\"No credentials provided. Skipping upload.\")\n            return False\n\n    if ensure_kaggle_creds(interactive=False):\n        model_path = Path(Config.OUTPUT_DIR).resolve()\n        if not model_path.exists():\n            raise FileNotFoundError(f\"Saved model directory not found: {model_path}. Run training and save the model first.\")\n\n        # Ensure .kaggleignore doesn't exclude files\n        ignore_file = model_path / \".kaggleignore\"\n        if ignore_file.exists():\n            ignore_file.unlink()\n\n        # Prepare minimal metadata\n        username = os.environ.get(\"KAGGLE_USERNAME\") or \"your-username\"\n        dataset_id = f\"{username}/{Config.DATASET_NAME}\"\n        \n        # Create detailed description with timestamp and config\n        upload_time = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n        description = (\n            f\"ByT5 fine-tuned model for DPC Akkadian->English.\\n\"\n            f\"Upload Time: {upload_time}\\n\"\n            f\"Epochs: {Config.EPOCHS}, LR: {Config.LEARNING_RATE}, Seed: {Config.SEED}\\n\"\n            f\"Training Batch: {Config.TRAIN_BATCH_SIZE}, Eval Batch: {Config.EVAL_BATCH_SIZE}\\n\"\n            f\"Base Model: {Config.MODEL_NAME}\"\n        )\n        \n        metadata = {\n            \"title\": Config.DATASET_NAME,\n            \"id\": dataset_id,\n            \"description\": description,\n            \"licenses\": [{\"name\": \"CC0-1.0\"}],\n        }\n\n        # Write metadata\n        metadata_file = model_path / \"dataset-metadata.json\"\n        with open(metadata_file, \"w\") as f:\n            json.dump(metadata, f, indent=2)\n\n        print(f\"Uploading {model_path} as dataset {dataset_id} (this may take a while)...\")\n\n        # Try create; if exists, create a new version\n        create = subprocess.run([\"kaggle\", \"datasets\", \"create\", \"-p\", str(model_path), \"--dir-mode\", \"zip\"], capture_output=True, text=True)\n        if create.returncode != 0:\n            stderr_l = (create.stderr or \"\").lower()\n            print(\"Create stderr:\", create.stderr)\n            if \"already exists\" in stderr_l or \"dataset with slug\" in stderr_l:\n                print(\"Dataset already exists. Creating a new version...\")\n                create = subprocess.run([\n                    \"kaggle\", \"datasets\", \"version\", \"-p\", str(model_path), \"-m\", \"New model version\", \"--dir-mode\", \"zip\", \"--force\"\n                ], capture_output=True, text=True)\n\n        print(\"=== Kaggle CLI stdout ===\")\n        print(create.stdout)\n        print(\"=== Kaggle CLI stderr ===\")\n        print(create.stderr)\n\n        if create.returncode == 0:\n            print(\"✅ Upload successful.\")\n        else:\n            print(\"❌ Upload failed. Inspect the error above.\")\n    else:\n        print(\"Skipping upload due to missing credentials.\")","metadata":{},"outputs":[],"execution_count":null},{"id":"53047bc9","cell_type":"markdown","source":"# Inference","metadata":{}},{"id":"4daca4a1","cell_type":"code","source":"if Config.MAKE_SUBMISSION:\n    print(\"Starting Inference...\")\n    from torch.utils.data import DataLoader, Dataset\n    \n    PREFIX = \"translate Akkadian to English: \"\n\n    class InferenceDataset(Dataset):\n        def __init__(self, df, tokenizer, max_length=Config.MAX_LENGTH):\n            self.texts = df['transliteration'].astype(str).tolist()\n            self.texts = [PREFIX + i for i in self.texts]\n            self.tokenizer = tokenizer\n            self.max_length = max_length\n            \n        def __len__(self):\n            return len(self.texts)\n        \n        def __getitem__(self, idx):\n            text = self.texts[idx]\n            inputs = self.tokenizer(\n                text, \n                max_length=self.max_length, \n                padding=\"max_length\", \n                truncation=True, \n                return_tensors=\"pt\"\n            )\n            return {\n                \"input_ids\": inputs[\"input_ids\"].squeeze(0),\n                \"attention_mask\": inputs[\"attention_mask\"].squeeze(0)\n            }\n\n    test_dataset = InferenceDataset(test_df, tokenizer)\n    test_loader = DataLoader(test_dataset, batch_size=Config.EVAL_BATCH_SIZE, shuffle=False)\n\n    DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    model = model.to(DEVICE)\n    model.eval()\n    model.float()  # force weights to FP32\n\n    all_predictions = []\n    torch.set_grad_enabled(False)\n\n    with torch.inference_mode():\n        for batch in test_loader:\n            input_ids = batch[\"input_ids\"].to(DEVICE)\n            attention_mask = batch[\"attention_mask\"].to(DEVICE)\n\n            outputs = model.generate(\n                input_ids=input_ids,\n                attention_mask=attention_mask,\n                max_length=Config.MAX_LENGTH,\n                num_beams=4,\n                early_stopping=True,\n            )\n\n            decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n            all_predictions.extend([d.strip() for d in decoded])\n\n    # Create submission file\n    submission = pd.DataFrame({\n        \"id\": test_df[\"id\"],\n        \"translation\": all_predictions\n    })\n\n    submission[\"translation\"] = submission[\"translation\"].apply(lambda x: x if len(x) > 0 else \"broken text\")\n    \n    submission.to_csv(\"submission.csv\", index=False)\n    print(\"✅ Submission file saved successfully!\")\n    print(submission.head())\nelse:\n    print(\"Inference disabled (Config.MAKE_SUBMISSION = False)\")","metadata":{},"outputs":[],"execution_count":null}]}