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Deep Past Initiative – Machine Translation\n\n## Takeaway\n\n- When the first letter of a word is capitalized it implies the word is a personal name or a place name (i.e. proper noun). When the word is in ALL CAPS, that implies it is a Sumerian logogram and was written in place of the Akkadian syllabic spelling for scribal simplicity.\n\n- Can use the published_text's transliteration as the cleaned version for training.\n\n- Can use the normed words in Lexicon to swap for training.\n\n## Facts\n\n- The transliteration words len < 150, translation words len < 300\n","metadata":{}},{"cell_type":"code","source":"!pip install -q evaluate sacrebleu","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2026-01-20T02:45:00.622405Z","iopub.execute_input":"2026-01-20T02:45:00.622728Z","iopub.status.idle":"2026-01-20T02:45:06.414901Z","shell.execute_reply.started":"2026-01-20T02:45:00.622701Z","shell.execute_reply":"2026-01-20T02:45:06.413968Z"},"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 matplotlib.pyplot as plt\nimport sentencepiece as spm\nfrom transformers import T5Tokenizer","metadata":{"execution":{"iopub.status.busy":"2026-01-20T02:45:06.417331Z","iopub.execute_input":"2026-01-20T02:45:06.417832Z","iopub.status.idle":"2026-01-20T02:45:41.323981Z","shell.execute_reply.started":"2026-01-20T02:45:06.417792Z","shell.execute_reply":"2026-01-20T02:45:41.322724Z"},"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 = 8       # Adjust depending on GPU memory (on a P100 you can usually go with 8–16).\n    EPOCHS = 100\n    LEARNING_RATE = 2e-4\n    OUTPUT_DIR = \"./byt5-akkadian-model\"\n\n    PATIENCE = 10\n\n    DRY_RUN = False","metadata":{"execution":{"iopub.status.busy":"2026-01-20T02:45:41.325138Z","iopub.execute_input":"2026-01-20T02:45:41.325947Z","iopub.status.idle":"2026-01-20T02:45:41.331765Z","shell.execute_reply.started":"2026-01-20T02:45:41.325915Z","shell.execute_reply":"2026-01-20T02:45:41.330733Z"},"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":"def 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":"2026-01-20T02:45:41.332954Z","iopub.execute_input":"2026-01-20T02:45:41.333351Z","iopub.status.idle":"2026-01-20T02:45:41.486634Z","shell.execute_reply.started":"2026-01-20T02:45:41.333315Z","shell.execute_reply":"2026-01-20T02:45:41.485694Z"},"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\")\n\ntrain_sent_df = pd.read_csv(\"/kaggle/input/dpc-sentence-alignment-oare-firstword/sentence_alignment.csv\")\ntrain_sent_df.rename(columns={\n    \"translit_sent\": \"transliteration\",\n    \"translation_sent_train\": \"translation\"\n}, inplace=True)\n\ntrain_df = pd.concat([train_df, train_sent_df[train_df.columns]])\n\nprint(f\"Train Data: {len(train_df)} docs.\")","metadata":{"execution":{"iopub.status.busy":"2026-01-20T02:45:41.487698Z","iopub.execute_input":"2026-01-20T02:45:41.488181Z","iopub.status.idle":"2026-01-20T02:45:41.600553Z","shell.execute_reply.started":"2026-01-20T02:45:41.488144Z","shell.execute_reply":"2026-01-20T02:45:41.599546Z"},"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":"# Build Corpus","metadata":{}},{"cell_type":"code","source":"SRC_COL = \"transliteration\"\nTGT_COL = \"translation\"\n\n# Normalize whitespace only (do NOT aggressively normalize yet)\ndef clean(s):\n    return \" \".join(str(s).split())\n\nwith open(\"spm_corpus.txt\", \"w\", encoding=\"utf-8\") as f:\n    for _, row in train_df.iterrows():\n        f.write(clean(row[SRC_COL]) + \"\\n\")\n        f.write(clean(row[TGT_COL]) + \"\\n\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-20T02:45:41.601588Z","iopub.execute_input":"2026-01-20T02:45:41.601844Z","iopub.status.idle":"2026-01-20T02:45:41.781307Z","shell.execute_reply.started":"2026-01-20T02:45:41.601821Z","shell.execute_reply":"2026-01-20T02:45:41.780375Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Sentencepiece Tokenizer","metadata":{}},{"cell_type":"code","source":"spm.SentencePieceTrainer.train(\n    input=\"/kaggle/working/spm_corpus.txt\",\n    model_prefix=\"akkadian_spm\",\n    vocab_size=3000,\n    model_type=\"unigram\",         # better for low-resource\n    character_coverage=1.0,\n    user_defined_symbols=[\n        \"<DIV>\", \"<NUM>\"\n    ],\n    bos_id=-1,\n    eos_id=1,\n    pad_id=0,\n    unk_id=2\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-20T02:45:41.783516Z","iopub.execute_input":"2026-01-20T02:45:41.784377Z","iopub.status.idle":"2026-01-20T02:45:43.041975Z","shell.execute_reply.started":"2026-01-20T02:45:41.784343Z","shell.execute_reply":"2026-01-20T02:45:43.041010Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Load Tokenizer","metadata":{}},{"cell_type":"code","source":"tokenizer = T5Tokenizer(\n    vocab_file=\"/kaggle/working/akkadian_spm.model\",\n    extra_ids=0,\n    pad_token=\"<pad>\",\n    eos_token=\"</s>\",\n    unk_token=\"<unk>\"\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-20T02:45:43.043168Z","iopub.execute_input":"2026-01-20T02:45:43.043465Z","iopub.status.idle":"2026-01-20T02:45:43.077938Z","shell.execute_reply.started":"2026-01-20T02:45:43.043423Z","shell.execute_reply":"2026-01-20T02:45:43.077289Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# T5 model for training","metadata":{}},{"cell_type":"code","source":"from transformers import T5Config, T5ForConditionalGeneration\n\nconfig = T5Config(\n    vocab_size=tokenizer.vocab_size,\n    d_model=256,  # token embedding size; 512 is the median size, a bigger size leads to overfitting\n    d_ff=1024,  # dimension of feed forward layer, rule of thumb = 4 * d_model\n    num_layers=6,  # number of blocks in the encoder\n    num_decoder_layers=6,\n    num_heads=8,\n    dropout_rate=0.1,\n    pad_token_id=tokenizer.pad_token_id,\n    eos_token_id=tokenizer.eos_token_id,\n    decoder_start_token_id=tokenizer.pad_token_id,\n)\n\nmodel = T5ForConditionalGeneration(config)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-20T02:45:43.079039Z","iopub.execute_input":"2026-01-20T02:45:43.079373Z","iopub.status.idle":"2026-01-20T02:45:43.825939Z","shell.execute_reply.started":"2026-01-20T02:45:43.079335Z","shell.execute_reply":"2026-01-20T02:45:43.824974Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Create Batched Dataset","metadata":{}},{"cell_type":"code","source":"from datasets import Dataset\n\ndef preprocess(examples):\n    prefix = \"translate Akkadian to English: \"\n\n    inputs = [prefix+ex for ex in examples[SRC_COL]]\n    targets = [ex for ex in examples[TGT_COL]]\n\n    model_inputs = tokenizer(\n        inputs,\n        max_length=Config.MAX_LENGTH,\n        truncation=True,\n        text_target=targets\n    )\n    return model_inputs\n    \ndataset = Dataset.from_pandas(train_df)\nsplit_dataset = dataset.train_test_split(test_size=0.15, seed=42)\n\ntrain_dataset = split_dataset[\"train\"].map(preprocess, batched=True)\nval_dataset = split_dataset[\"test\"].map(preprocess, batched=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-20T02:45:43.826964Z","iopub.execute_input":"2026-01-20T02:45:43.827304Z","iopub.status.idle":"2026-01-20T02:45:46.489463Z","shell.execute_reply.started":"2026-01-20T02:45:43.827274Z","shell.execute_reply":"2026-01-20T02:45:46.488840Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Training","metadata":{}},{"cell_type":"code","source":"from sacrebleu.metrics import CHRF\nimport numpy as np\n\n# update the metrics as competition metrics collapse during training phase\nchrf = CHRF(word_order=2)\n\ndef compute_metrics(eval_preds):\n    preds, labels = eval_preds\n    if isinstance(preds, tuple):\n        preds = preds[0]\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    # sentence-level chrF (macro)\n    sent_scores = [\n        chrf.sentence_score(p.strip(), [l.strip()]).score\n        for p, l in zip(decoded_preds, decoded_labels)\n    ]\n\n    return {\n        \"sentence_chrf\": float(np.mean(sent_scores))\n    }\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-20T03:15:33.833957Z","iopub.execute_input":"2026-01-20T03:15:33.834244Z","iopub.status.idle":"2026-01-20T03:15:33.840003Z","shell.execute_reply.started":"2026-01-20T03:15:33.834215Z","shell.execute_reply":"2026-01-20T03:15:33.839278Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments, EarlyStoppingCallback, GenerationConfig\n\n# GenerationConfig(\n#     max_length=512,\n#     num_beams=4,\n#     no_repeat_ngram_size=4,\n#     repetition_penalty=1.3,\n#     length_penalty=1.1,\n#     early_stopping=True,\n# )\n\n\nEPOCH = 3 if Config.DRY_RUN else Config.EPOCHS\ndata_collator = DataCollatorForSeq2Seq(tokenizer, model=model)\n\ntraining_args = Seq2SeqTrainingArguments(\n    output_dir=Config.OUTPUT_DIR,\n    eval_strategy=\"epoch\",\n    save_strategy=\"epoch\",\n    learning_rate=Config.LEARNING_RATE,      # higher LR since training from scratch\n    per_device_train_batch_size=Config.BATCH_SIZE,\n    per_device_eval_batch_size=Config.BATCH_SIZE,\n    weight_decay=0.01,\n    save_total_limit=2,\n    num_train_epochs=EPOCH,\n    fp16=True,\n    logging_steps=100,\n    report_to=\"none\",\n    # ===== EARLY STOPPING CONFIG =====\n    metric_for_best_model=\"eval_loss\",\n    load_best_model_at_end=True,\n    greater_is_better=False,\n    # predict_with_generate=True,\n)\n\ntrainer = Seq2SeqTrainer(\n    model=model,\n    args=training_args,\n    train_dataset=train_dataset,\n    eval_dataset=val_dataset,\n    tokenizer=tokenizer,\n    data_collator=data_collator,\n    # early stopping\n    # compute_metrics=compute_metrics,\n    callbacks=[EarlyStoppingCallback(early_stopping_patience=Config.PATIENCE)],\n\n)\n\ntrainer.train()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-20T03:15:35.384274Z","iopub.execute_input":"2026-01-20T03:15:35.384580Z","iopub.status.idle":"2026-01-20T03:19:32.988880Z","shell.execute_reply.started":"2026-01-20T03:15:35.384551Z","shell.execute_reply":"2026-01-20T03:19:32.988144Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Save","metadata":{}},{"cell_type":"code","source":"trainer.save_model(Config.OUTPUT_DIR)\ntokenizer.save_pretrained(Config.OUTPUT_DIR)\nprint(f\"Model saved to {Config.OUTPUT_DIR}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-20T02:49:53.010802Z","iopub.execute_input":"2026-01-20T02:49:53.011150Z","iopub.status.idle":"2026-01-20T02:49:53.364920Z","shell.execute_reply.started":"2026-01-20T02:49:53.011111Z","shell.execute_reply":"2026-01-20T02:49:53.364192Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# predictions = trainer.predict(val_dataset)\n# labels = predictions.label_ids\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_labels = [[label.strip()] for label in decoded_labels]\n# pred_ids = predictions.predictions\n\n# if isinstance(pred_ids, tuple):\n#     pred_ids = pred_ids[0]\n\n# decoded_preds = tokenizer.batch_decode(\n#     pred_ids,\n#     skip_special_tokens=True,\n#     clean_up_tokenization_spaces=True,\n# )\n# decoded_preds = [pred.strip() for pred in decoded_preds]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-20T03:19:41.102851Z","iopub.execute_input":"2026-01-20T03:19:41.103145Z","iopub.status.idle":"2026-01-20T03:19:59.242135Z","shell.execute_reply.started":"2026-01-20T03:19:41.103120Z","shell.execute_reply":"2026-01-20T03:19:59.241534Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Dry run Inference","metadata":{}},{"cell_type":"code","source":"from torch.utils.data import DataLoader, Dataset\nfrom tqdm import tqdm\n\nMAX_LENGTH = 512\nDEVICE = \"cuda\"\n\ntokenizer_loaded = AutoTokenizer.from_pretrained(Config.OUTPUT_DIR)\nmodel_loaded = AutoModelForSeq2SeqLM.from_pretrained(Config.OUTPUT_DIR).to(\"cuda\")\nmodel_loaded.eval()\n\n\nclass InferenceDataset(Dataset):\n    def __init__(self, df, tokenizer):\n        self.texts = df['transliteration'].astype(str).tolist()\n        self.texts = [\"translate Akkadian to English: \"+i for i in self.texts]\n        self.translation = df['translation'].astype(str).tolist()\n        self.tokenizer = tokenizer\n        \n    def __len__(self):\n        return len(self.texts)\n    \n    def __getitem__(self, idx):\n        text = self.texts[idx]\n        translation = self.translation[idx]\n        inputs = self.tokenizer(\n            text, \n            max_length=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            \"translation\": translation\n        }\n\ntest_df = train_df.sample(5)\ntest_dataset = InferenceDataset(test_df, tokenizer_loaded)\ntest_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)\n\n\nprint(\"Starting Inference...\")\nall_predictions = []\nactuals = []\nwith torch.no_grad():\n    for batch in tqdm(test_loader):\n        input_ids = batch[\"input_ids\"].to(DEVICE)\n        attention_mask = batch[\"attention_mask\"].to(DEVICE)\n        actuals.extend(batch['translation'])\n  \n        outputs = model_loaded.generate(\n            input_ids=input_ids,\n            attention_mask=attention_mask,\n            max_length=MAX_LENGTH,\n            do_sample=False,\n            # num_beams=4,\n            # early_stopping=True,\n            # top_k=30, \n            # top_p=0.95\n        )\n        \n        decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n        all_predictions.extend([d.strip() for d in decoded])\n\npred = pd.DataFrame(\n    {\n        \"predictions\": all_predictions,\n        \"actuals\": actuals\n    }\n    \n)\npred","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-20T02:49:53.365898Z","iopub.execute_input":"2026-01-20T02:49:53.366210Z","iopub.status.idle":"2026-01-20T02:49:58.514304Z","shell.execute_reply.started":"2026-01-20T02:49:53.366174Z","shell.execute_reply":"2026-01-20T02:49:58.513341Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}