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Deep Past (Lexicon Training Notebook)","metadata":{}},{"cell_type":"markdown","source":"## Notebook setup","metadata":{}},{"cell_type":"code","source":"!pip install evaluate sacrebleu","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2026-03-21T16:18:42.890152Z","iopub.execute_input":"2026-03-21T16:18:42.890725Z","iopub.status.idle":"2026-03-21T16:18:48.156327Z","shell.execute_reply.started":"2026-03-21T16:18:42.890693Z","shell.execute_reply":"2026-03-21T16:18:48.155617Z"},"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 unicodedata\nimport random\nfrom torch.utils.data import DataLoader, Sampler","metadata":{"execution":{"iopub.status.busy":"2026-03-21T16:18:48.158110Z","iopub.execute_input":"2026-03-21T16:18:48.158443Z","iopub.status.idle":"2026-03-21T16:19:21.703028Z","shell.execute_reply.started":"2026-03-21T16:18:48.158406Z","shell.execute_reply":"2026-03-21T16:19:21.702415Z"},"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-base\" \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 = 2\n    LEARNING_RATE = 1e-4\n    \n    OUTPUT_DIR = \"./byt5-akkadian-model-pretrained\"","metadata":{"execution":{"iopub.status.busy":"2026-03-21T16:19:21.703864Z","iopub.execute_input":"2026-03-21T16:19:21.704505Z","iopub.status.idle":"2026-03-21T16:19:21.708853Z","shell.execute_reply.started":"2026-03-21T16:19:21.704478Z","shell.execute_reply":"2026-03-21T16:19:21.707974Z"},"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":"2026-03-21T16:19:21.710910Z","iopub.execute_input":"2026-03-21T16:19:21.711256Z","iopub.status.idle":"2026-03-21T16:19:21.732734Z","shell.execute_reply.started":"2026-03-21T16:19:21.711234Z","shell.execute_reply":"2026-03-21T16:19:21.731892Z"},"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\"\nlexicon = pd.read_csv(f\"{INPUT_DIR}/OA_Lexicon_eBL.csv\")","metadata":{"execution":{"iopub.status.busy":"2026-03-21T16:19:21.733870Z","iopub.execute_input":"2026-03-21T16:19:21.734157Z","iopub.status.idle":"2026-03-21T16:19:21.858921Z","shell.execute_reply.started":"2026-03-21T16:19:21.734132Z","shell.execute_reply":"2026-03-21T16:19:21.858251Z"},"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":"markdown","source":"## Lexicon inspection","metadata":{}},{"cell_type":"code","source":"# Seperate PN and GN from words\nPN_GN = lexicon[lexicon['type'].isin(['PN', 'GN'])].reset_index(drop = True)\n\nPN_GN.sample(10, random_state = 42)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-21T16:19:21.859873Z","iopub.execute_input":"2026-03-21T16:19:21.860228Z","iopub.status.idle":"2026-03-21T16:19:21.898835Z","shell.execute_reply.started":"2026-03-21T16:19:21.860189Z","shell.execute_reply":"2026-03-21T16:19:21.898207Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# This example illustrates that we need the lexeme column\n\n# Tranliteration: ... i-dí-IŠTAR ...\n# -->\n# Translation: ... Iddin-Ištar ...\n\nPN_GN[PN_GN['form'] == 'i-dí-IŠTAR']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-21T16:19:21.899723Z","iopub.execute_input":"2026-03-21T16:19:21.900056Z","iopub.status.idle":"2026-03-21T16:19:21.911270Z","shell.execute_reply.started":"2026-03-21T16:19:21.900033Z","shell.execute_reply":"2026-03-21T16:19:21.910564Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Preprocessing class","metadata":{}},{"cell_type":"code","source":"class AkkadianPreprocessor:\n    \"\"\"Normalize Akkadian transliteration text.\"\"\"\n    def __init__(self):\n        # Subscript numbers (convert to regular digits)\n        self.subscript_trans = str.maketrans(\n            '₀₁₂₃₄₅₆₇₈₉',\n            '0123456789'\n        )\n\n        # Map accented characters to a consistent format\n        self.char_map = str.maketrans({\n            'á': 'a2', 'à': 'a3',\n            'é': 'e2', 'è': 'e3',\n            'í': 'i2', 'ì': 'i3',\n            'ú': 'u2', 'ù': 'u3',\n            'š': 'sz', 'Š': 'SZ',\n            'ṣ': 's,', 'Ṣ': 'S,',\n            'ṭ': 't,', 'Ṭ': 'T,',\n            'ḫ': 'h', 'Ḫ': 'H',\n            'ʾ': \"'\"\n        })\n\n        # Map known decimal endings to fraction symbols\n        self.fraction_map = {\n            \"5\": \"½\",\n            \"25\": \"¼\",\n            \"75\": \"¾\",\n            \"3333\": \"⅓\",\n            \"6666\": \"⅔\",\n            \"1666\": \"⅙\",\n            \"8333\": \"⅚\",\n            \"625\": \"⅝\",\n        }\n\n        # Bracketed content (such as editorial notes or uncertain reconstructions)\n        self.square_brackets = re.compile(r'\\[\\s*(.*?)\\s*\\]')\n        self.editorial_brackets = re.compile(r'˹\\s*(.*?)\\s*˺')\n        self.angle_brackets = re.compile(r'<\\s*(.*?)\\s*>')\n\n        # Stray Hebrew characters\n        self.hebrew_chars = re.compile(r'[\\u0590-\\u05FF]')\n\n        # Extra whitespace\n        self.whitespace = re.compile(r'\\s+')\n\n    def preprocess_batch(self, texts):\n        s = pd.Series(texts).fillna('').astype(str)\n\n        s = s.apply(lambda x: unicodedata.normalize(\"NFC\", x))\n\n        # Preserve content inside brackets\n        s = s.str.replace(self.editorial_brackets, r'\\1', regex=True)\n        s = s.str.replace(self.square_brackets, r'\\1', regex=True)\n        s = s.str.replace(self.angle_brackets, r'\\1', regex=True)\n\n        # Subscript numbers\n        s = s.str.translate(self.subscript_trans)\n\n        # Normalize diacritics\n        s = s.str.translate(self.char_map)\n\n        # Remove stray Hebrew\n        s = s.str.replace(self.hebrew_chars, '', regex=True)\n\n        # Lexical normalization\n        s = s.str.replace('KÙ.B.', 'KÙ.BABBAR', regex=False)\n\n        for dec, frac in self.fraction_map.items():\n            # integer.decimal\n            s = s.str.replace(\n                rf'\\b(\\d+)\\.{dec}\\b',\n                lambda m: (\n                    frac if m.group(1) == \"0\"\n                    else f\"{m.group(1)} {frac}\"\n                ),\n                regex=True\n            )\n\n        # Collapse whitespace\n        s = s.str.replace(self.whitespace, ' ', regex=True).str.strip()\n\n        return s.tolist()\n\nakkadian_preprocessor = AkkadianPreprocessor()\nprint(\"Preprocessors ready\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-21T16:19:21.912082Z","iopub.execute_input":"2026-03-21T16:19:21.912393Z","iopub.status.idle":"2026-03-21T16:19:21.926463Z","shell.execute_reply.started":"2026-03-21T16:19:21.912331Z","shell.execute_reply":"2026-03-21T16:19:21.925666Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Create PN/GN training data (person name / geographic name)","metadata":{}},{"cell_type":"code","source":"def create_bidirectional_data(dataset_split, source = 'transliteration', target = 'translation'):\n    df = dataset_split.to_pandas()\n\n    # Apply preprocessing to both source and target texts before creating the bidirectional pairs\n    df[source] = akkadian_preprocessor.preprocess_batch(df[source].tolist())\n    \n    # Direction 1: Akkadian -> English (original task)\n    df_fwd = df.copy()\n    df_fwd['input_text'] = \"translate Akkadian to English: \" + df_fwd[source].astype(str)\n    df_fwd['target_text'] = df_fwd[target].astype(str)\n    \n    # Direction 2: English -> Akkadian (back-translation task)\n    df_bwd = df.copy()\n    df_bwd['input_text'] = \"translate English to Akkadian: \" + df_bwd[target].astype(str)\n    df_bwd['target_text'] = df_bwd[source].astype(str)\n    \n    # Combine and shuffle\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, source = 'transliteration', target = 'translation'):\n    df = dataset_split.to_pandas()\n    \n    # Apply preprocessing to both source and target texts before creating the bidirectional pairs\n    df[source] = akkadian_preprocessor.preprocess_batch(df[source].tolist())\n    \n    # For validation: only align format (Akkadian -> English only)\n    df['input_text'] = \"translate Akkadian to English: \" + df[source].astype(str)\n    df['target_text'] = df[target].astype(str)\n    return Dataset.from_pandas(df)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-21T16:19:21.927295Z","iopub.execute_input":"2026-03-21T16:19:21.927588Z","iopub.status.idle":"2026-03-21T16:19:21.941411Z","shell.execute_reply.started":"2026-03-21T16:19:21.927560Z","shell.execute_reply":"2026-03-21T16:19:21.940615Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Add pre-fixes, make bidirectional & convert to dataset object\n\n# Convert to Hugging Face Dataset format & split into Train/Val with test_size = 0.1.\nPN_GN_split = Dataset.from_pandas(PN_GN).train_test_split(test_size = 0.1, seed = 42)\n\n# Make training data bidirectional (doubles the data size)\nPN_GN_train = create_bidirectional_data(PN_GN_split[\"train\"], 'form', 'lexeme')\n\n# Keep validation data unidirectional (for evaluation purposes)\nPN_GN_val = create_unidirectional_data(PN_GN_split[\"test\"], 'form', 'lexeme')\n\nprint(f\"Train samples: {len(PN_GN_train)} (Bidirectional)\")\nprint(f\"Val samples:   {len(PN_GN_val)} (Unidirectional)\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-21T16:19:21.943399Z","iopub.execute_input":"2026-03-21T16:19:21.943697Z","iopub.status.idle":"2026-03-21T16:19:22.799187Z","shell.execute_reply.started":"2026-03-21T16:19:21.943674Z","shell.execute_reply":"2026-03-21T16:19:22.798376Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Tokenise data & Initialise","metadata":{}},{"cell_type":"code","source":"# ==========================================\n# 3. Tokenization & preprocessing\n# ==========================================\ntokenizer = AutoTokenizer.from_pretrained(Config.MODEL_NAME)\n\ndef preprocess_function(examples):\n    # Use the \"input_text\" and \"target_text\" created with prefix during data creation\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# Apply map to the newly created datasets\ntokenized_PN_GN_train = PN_GN_train.map(preprocess_function, batched=True)\ntokenized_PN_GN_val = PN_GN_val.map(preprocess_function, batched=True)","metadata":{"execution":{"iopub.status.busy":"2026-03-21T16:19:22.800042Z","iopub.execute_input":"2026-03-21T16:19:22.800450Z","iopub.status.idle":"2026-03-21T16:19:26.444817Z","shell.execute_reply.started":"2026-03-21T16:19:22.800414Z","shell.execute_reply":"2026-03-21T16:19:26.444049Z"},"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)\ndata_collator = DataCollatorForSeq2Seq(tokenizer, model=model)\n\n# Prepare evaluation metrics\nmetric_chrf = evaluate.load(\"chrf\")\nmetric_bleu = evaluate.load(\"sacrebleu\")\n\ndef compute_metrics(eval_preds):\n    preds, labels = eval_preds\n\n    # Safety check: Seq2SeqTrainer/Trainer may return a tuple\n    if isinstance(preds, tuple):\n        preds = preds[0]\n\n    # ★ Important ★\n    # If preds are logits: (batch, seq, vocab) → convert to token ids with argmax\n    if hasattr(preds, \"ndim\") and preds.ndim == 3:\n        preds = np.argmax(preds, axis=-1)\n\n    # Convert to int and handle out-of-range values (ByT5 decode uses chr)\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    # Existing evaluation computation (adapt to your continuation processing)\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}","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-21T16:19:26.445861Z","iopub.execute_input":"2026-03-21T16:19:26.446172Z","iopub.status.idle":"2026-03-21T16:19:41.431572Z","shell.execute_reply.started":"2026-03-21T16:19:26.446147Z","shell.execute_reply":"2026-03-21T16:19:41.430901Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Train on PN/GN pairs","metadata":{}},{"cell_type":"code","source":"args = 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    generation_max_length=Config.MAX_LENGTH,\n    generation_num_beams=4,\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_PN_GN_train,\n    eval_dataset=tokenized_PN_GN_val,\n    data_collator=data_collator,\n    tokenizer=tokenizer,\n    compute_metrics=compute_metrics\n)\n\nprint(\"Starting Training (FP32 mode)...\")\ntrainer.train()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-21T16:19:41.432619Z","iopub.execute_input":"2026-03-21T16:19:41.433216Z","execution_failed":"2026-03-21T16:20:06.617Z"}},"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":"2026-03-21T16:20:06.617Z"},"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}]}