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==========================================\n# 1. INSTALLATION & SETUP\n# ==========================================\n!pip install -q transformers datasets sacrebleu accelerate sentencepiece\n\nimport pandas as pd\nimport numpy as np\nimport os\nimport shutil\nimport sacrebleu\nfrom datasets import Dataset, DatasetDict\nfrom sklearn.model_selection import train_test_split\nfrom transformers import (\n AutoTokenizer, \n AutoModelForSeq2SeqLM, \n DataCollatorForSeq2Seq, \n Seq2SeqTrainingArguments, \n Seq2SeqTrainer\n)\n\n# --- CRITICAL: FREE UP DISK SPACE BEFORE STARTING ---\n# We delete the old model folder completely to prevent \"Disk Full\" errors\nif os.path.exists(\"byt5-akkadian-final\"):\n shutil.rmtree(\"byt5-akkadian-final\", ignore_errors=True)\nprint(\"Disk space cleaned.\")\n\n# ==========================================\n# 2. DATASET GENERATION\n# ==========================================\nTRAIN_INPUT = \"/kaggle/input/deep-past-initiative-machine-translation/train.csv\"\nSENTENCES_INPUT = \"/kaggle/input/deep-past-initiative-machine-translation/Sentences_Oare_FirstWord_LinNum.csv\"\nCLEAN_FILE = \"final_train_cleaned.csv\"\n\ndef clean_akkadian(text):\n if not isinstance(text, str): return \"\"\n text = text.replace(\"!\", \"\").replace(\"?\", \"\").replace(\"/\", \"\")\n text = text.replace(\"[\", \"\").replace(\"]\", \"\").replace(\"<\", \"\").replace(\">\", \"\") \n text = text.replace(\":\", \" \").replace(\".\", \" \") \n subs = {\"₀\":\"0\", \"₁\":\"1\", \"₂\":\"2\", \"₃\":\"3\", \"₄\":\"4\", \"₅\":\"5\", \"₆\":\"6\", \"₇\":\"7\", \"₈\":\"8\", \"₉\":\"9\"}\n for k, v in subs.items(): text = text.replace(k, v)\n return \" \".join(text.split())\n\ndef clean_english(text):\n if not isinstance(text, str): return \"\"\n return \" \".join(text.split())\n\ndef make_dataset():\n if os.path.exists(CLEAN_FILE):\n return pd.read_csv(CLEAN_FILE)\n \n print(\"Generating clean dataset...\")\n df_train = pd.read_csv(TRAIN_INPUT)\n df_sent = pd.read_csv(SENTENCES_INPUT)\n aligned_data = []\n valid_uuids = set(df_sent['text_uuid'].unique())\n\n # Process Aligned Sentences\n for _, row in df_train[df_train['oare_id'].isin(valid_uuids)].iterrows():\n doc_id = row['oare_id']\n words = row['transliteration'].split() if isinstance(row['transliteration'], str) else []\n doc_sents = df_sent[df_sent['text_uuid'] == doc_id].dropna(subset=['first_word_number']).sort_values('first_word_number')\n starts = doc_sents['first_word_number'].astype(int).tolist()\n trans = doc_sents['translation'].tolist()\n for i in range(len(starts)):\n start_idx = starts[i] - 1\n end_idx = starts[i+1] - 1 if i < len(starts) - 1 else len(words)\n if start_idx < len(words) and end_idx > start_idx:\n chunk = \" \".join(words[start_idx:end_idx])\n aligned_data.append({'transliteration': clean_akkadian(chunk), 'translation': clean_english(trans[i])})\n\n # Process Documents\n for _, row in df_train[~df_train['oare_id'].isin(valid_uuids)].iterrows():\n aligned_data.append({'transliteration': clean_akkadian(row['transliteration']), 'translation': clean_english(row['translation'])})\n\n final_df = pd.DataFrame(aligned_data)\n final_df = final_df[final_df['transliteration'].str.len() > 2]\n final_df.to_csv(CLEAN_FILE, index=False)\n return final_df\n\ndf = make_dataset()\n\n# ==========================================\n# 3. MODEL & TOKENIZER\n# ==========================================\nMODEL_CHECKPOINT = \"google/byt5-base\"\nMAX_LENGTH = 512\n\ntokenizer = AutoTokenizer.from_pretrained(MODEL_CHECKPOINT)\nmodel = AutoModelForSeq2SeqLM.from_pretrained(MODEL_CHECKPOINT)\n\ntrain_df, val_df = train_test_split(df, test_size=0.1, random_state=42)\ndataset = DatasetDict({\"train\": Dataset.from_pandas(train_df), \"validation\": Dataset.from_pandas(val_df)})\n\ndef preprocess_function(examples):\n inputs = [str(x) for x in examples[\"transliteration\"]]\n targets = [str(x) for x in examples[\"translation\"]]\n model_inputs = tokenizer(inputs, max_length=MAX_LENGTH, truncation=True)\n with tokenizer.as_target_tokenizer():\n labels = tokenizer(targets, max_length=MAX_LENGTH, truncation=True)\n model_inputs[\"labels\"] = labels[\"input_ids\"]\n return model_inputs\n\ntokenized_datasets = dataset.map(preprocess_function, batched=True)\n\n# ==========================================\n# 4. METRIC (Crash Proof)\n# ==========================================\ndef compute_metrics(eval_preds):\n preds, labels = eval_preds\n if isinstance(preds, tuple): preds = preds[0]\n \n preds = np.where(preds != -100, preds, tokenizer.pad_token_id)\n labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n \n decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n decoded_preds = [p.strip() for p in decoded_preds]\n decoded_labels = [[l.strip()] for l in decoded_labels]\n \n bleu = sacrebleu.corpus_bleu(decoded_preds, decoded_labels).score\n chrf = sacrebleu.corpus_chrf(decoded_preds, decoded_labels, word_order=2).score\n final_score = np.sqrt(bleu * chrf + 1e-9)\n \n return {\"score\": final_score, \"bleu\": bleu, \"chrf\": chrf}\n\n# ==========================================\n# 5. TRAINING (Disk Safe + Score Stabilized)\n# ==========================================\nargs = Seq2SeqTrainingArguments(\n output_dir=\"byt5-akkadian-final\",\n \n # --- STABILITY SETTINGS (INCREMENTING SCORE) ---\n fp16=False, \n learning_rate=4e-5, # <--- LOWERED to 4e-5. This will make the score climb STEADILY.\n weight_decay=0.01, # <--- Added to prevent overfitting/score drops\n \n # --- DISK SPACE SAFETY ---\n save_only_model=True, # <--- THIS FIXES THE CRASH. Saves 60% disk space.\n save_total_limit=1,\n \n # --- BATCHING ---\n per_device_train_batch_size=2,\n per_device_eval_batch_size=2,\n gradient_accumulation_steps=16,\n gradient_checkpointing=True,\n \n # --- LOGGING ---\n eval_strategy=\"epoch\",\n save_strategy=\"epoch\",\n load_best_model_at_end=True,\n metric_for_best_model=\"score\",\n greater_is_better=True,\n num_train_epochs=15,\n predict_with_generate=True,\n generation_max_length=512,\n logging_steps=20,\n report_to=\"none\"\n)\n\ntrainer = Seq2SeqTrainer(\n model,\n args,\n train_dataset=tokenized_datasets[\"train\"],\n eval_dataset=tokenized_datasets[\"validation\"],\n data_collator=DataCollatorForSeq2Seq(tokenizer, model=model),\n tokenizer=tokenizer,\n compute_metrics=compute_metrics\n)\n\nprint(\"Starting Disk-Safe & Stabilized Training...\")\ntrainer.train()\n\ntrainer.save_model(\"best_stable_model\")\nprint(\"SUCCESS: Best model saved to 'best_stable_model'\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-22T17:20:15.767141Z","iopub.execute_input":"2026-01-22T17:20:15.767911Z"}},"outputs":[],"execution_count":null}]}