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Configuration","metadata":{"papermill":{"duration":0.011661,"end_time":"2026-01-07T10:22:18.128534","exception":false,"start_time":"2026-01-07T10:22:18.116873","status":"completed"},"tags":[]}},{"id":"701bc954","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)\nimport random\nfrom sentence_transformers import SentenceTransformer, util\nimport evaluate\nimport re\nimport numpy as np\nimport pandas as pd\nfrom typing import List\nfrom datasets import Dataset\nfrom peft import get_peft_model\nfrom transformers import BitsAndBytesConfig,TorchAoConfig, AutoModelForSeq2SeqLM, AutoTokenizer, Seq2SeqTrainingArguments, Seq2SeqTrainer, DataCollatorForSeq2Seq , EarlyStoppingCallback\nfrom peft import LoraModel, LoraConfig\nimport 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,math\nfrom peft import prepare_model_for_kbit_training\nfrom tqdm import tqdm\nimport sentencepiece as spm\nfrom transformers import T5Tokenizer\nfrom transformers import EarlyStoppingCallback\nfrom tqdm import tqdm\nfrom transformers import TrainingArguments\nfrom transformers import Trainer","metadata":{"papermill":{"duration":32.355252,"end_time":"2026-01-07T10:22:55.459025","exception":false,"start_time":"2026-01-07T10:22:23.103773","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"259bd2dc-32d2-4b37-be15-bae33dbc5ac9","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"id":"11854e22","cell_type":"code","source":"class Config:\n \"\"\"Configuration for ByT5 Akkadian-to-English Machine Translation\"\"\"\n # ====== Model Configuration ======\n MODEL_NAME = \"google/byt5-base\" #\"google/byt5-base\"\n # PRETRAINED_DIR = None # Set to a path to resume training; None to start from scratch\n PRETRAINED_DIR = None\n MAX_LENGTH = 512\n MAX_LENGTH_LABELS = 768\n MAX_LENGTH_INFER = 768\n # ====== Training Configuration ======\n INPUT_DIR = \"./\"\n SEED = 42\n EPOCHS = 30\n LEARNING_RATE = 2e-4\n TRAIN_BATCH_SIZE = 4 # per_device_train_batch_size\n EVAL_BATCH_SIZE = 4 # per_device_eval_batch_size\n GRADIENT_ACCUMULATION_STEPS = 2\n OUTPUT_DIR = \"./byt5-akkadian-model-xavier-stage2-newtry\"\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 = \"\"\n KAGGLE_KEY = \"\"\n DATASET_NAME = \"byt5-akkadian-model-private\"","metadata":{"papermill":{"duration":0.02017,"end_time":"2026-01-07T10:22:55.492848","exception":false,"start_time":"2026-01-07T10:22:55.472678","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"9f92fcf8","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":{"papermill":{"duration":0.024181,"end_time":"2026-01-07T10:22:55.52967","exception":false,"start_time":"2026-01-07T10:22:55.505489","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"66320ca7-18b0-4857-b7f4-f44af7c18716","cell_type":"code","source":"def preprocess_input_text(text):\n # Handle NaN\n if pd.isna(text):\n return \"\"\n\n # Convert to string\n cleaned_text = str(text)\n\n # Replace gaps\n cleaned_text = re.compile(r\"(\\.{3,}|…+|……)\").sub(\"\", cleaned_text)\n cleaned_text = re.compile(r\"(xx+|\\s+x\\s+)\").sub(\"\", cleaned_text)\n\n return cleaned_text\n# https://www.kaggle.com/code/pshikk/ffn-lora-deep-past\n_V2 = re.compile(r\"([aAeEiIuU])(?:2|₂)\")\n_V3 = re.compile(r\"([aAeEiIuU])(?:3|₃)\")\n_ACUTE = str.maketrans({\"a\":\"á\",\"e\":\"é\",\"i\":\"í\",\"u\":\"ú\",\"A\":\"Á\",\"E\":\"É\",\"I\":\"Í\",\"U\":\"Ú\"})\n_GRAVE = str.maketrans({\"a\":\"à\",\"e\":\"è\",\"i\":\"ì\",\"u\":\"ù\",\"A\":\"À\",\"E\":\"È\",\"I\":\"Ì\",\"U\":\"Ù\"})\n\ndef _ascii_to_diacritics(s: str) -> str:\n s = s.replace(\"sz\", \"š\").replace(\"SZ\", \"Š\")\n s = s.replace(\"s,\", \"ṣ\").replace(\"S,\", \"Ṣ\")\n s = s.replace(\"t,\", \"ṭ\").replace(\"T,\", \"Ṭ\")\n s = _V2.sub(lambda m: m.group(1).translate(_ACUTE), s)\n s = _V3.sub(lambda m: m.group(1).translate(_GRAVE), s)\n return s\n\n\n_ALLOWED_FRACS = [\n (1/6, \"0.16666\"), (1/4, \"0.25\"), (1/3, \"0.33333\"),\n (1/2, \"0.5\"), (2/3, \"0.66666\"), (3/4, \"0.75\"), (5/6, \"0.83333\"),\n]\n_FRAC_TOL = 2e-3\n_FLOAT_RE = re.compile(r\"(? str:\n ip = int(math.floor(x + 1e-12))\n frac = x - ip\n best = min(_ALLOWED_FRACS, key=lambda t: abs(frac - t[0]))\n\n if abs(frac - best[0]) <= _FRAC_TOL:\n dec = best[1]\n if ip == 0:\n return dec\n return f\"{ip}{dec[1:]}\" if dec.startswith(\"0.\") else f\"{ip}+{dec}\"\n\n return f\"{x:.5f}\".rstrip(\"0\").rstrip(\".\")\n\n\n_TAG_BIGGAP_RE = re.compile(r\"<\\s*big[\\s_\\-]*gap\\s*>\", re.I)\n_TAG_GAP_RE = re.compile(r\"<\\s*gap\\s*>\", re.I)\n_BARE_BIGGAP = re.compile(r\"\\bbig[\\s_\\-]*gap\\b\", re.I)\n_ELLIPSIS_RE = re.compile(r\"(?:\\.{3,}|…+|\\[\\.+\\])\")\n_BRACKET_X_RE = re.compile(r\"(\\[\\s*x\\s*\\]|\\(\\s*x\\s*\\))\", re.I)\n_XTOKEN_RUN_RE = re.compile(r\"\\bx(?:\\s+x)+\\b\", re.I)\n_XRUN_RE = re.compile(r\"(?\"\n r\"|<\\s*gap\\s*>\"\n r\"|\\bbig[\\s_\\-]*gap\\b\"\n r\"|\\bx(?:\\s+x)+\\b\"\n r\"|\\.{3,}|…+|\\[\\.+\\]\"\n r\"|\\[\\s*x\\s*\\]|\\(\\s*x\\s*\\)\"\n r\"|(? pd.Series:\n return ser.str.replace(_GAP_UNIFIED_RE, \"\", regex=True)\n\n\n_CHAR_TRANS = str.maketrans({\n \"ḫ\":\"h\",\"Ḫ\":\"H\",\"ʾ\":\"\",\n \"₀\":\"0\",\"₁\":\"1\",\"₂\":\"2\",\"₃\":\"3\",\"₄\":\"4\",\n \"₅\":\"5\",\"₆\":\"6\",\"₇\":\"7\",\"₈\":\"8\",\"₉\":\"9\",\n \"—\":\"-\",\"–\":\"-\",\n})\n_SUB_X = \"ₓ\"\n\n_UNICODE_UPPER = r\"A-ZŠṬṢḪ\\u00C0-\\u00D6\\u00D8-\\u00DE\\u0160\\u1E00-\\u1EFF\"\n_UNICODE_LOWER = r\"a-zšṭṣḫ\\u00E0-\\u00F6\\u00F8-\\u00FF\\u0161\\u1E01-\\u1EFF\"\n\n_DET_UPPER_RE = re.compile(r\"\\(([\" + _UNICODE_UPPER + r\"0-9]{1,6})\\)\")\n_DET_LOWER_RE = re.compile(r\"\\(([\" + _UNICODE_LOWER + r\"]{1,4})\\)\")\n\n_PN_RE = re.compile(r\"\\bPN\\b\")\n_KUBABBAR_RE = re.compile(r\"KÙ\\.B\\.\")\n\n_EXACT_FRAC_RE = re.compile(r\"0\\.8333|0\\.6666|0\\.3333|0\\.1666|0\\.625|0\\.75|0\\.25|0\\.5\")\n_EXACT_FRAC_MAP = {\n \"0.8333\": \"⅚\", \"0.6666\": \"⅔\", \"0.3333\": \"⅓\", \"0.1666\": \"⅙\",\n \"0.625\": \"⅝\", \"0.75\": \"¾\", \"0.25\": \"¼\", \"0.5\": \"½\",\n}\n\ndef _frac_repl(m: re.Match) -> str:\n return _EXACT_FRAC_MAP[m.group(0)]\n\n\nclass OptimizedPreprocessor:\n def preprocess_batch(self, texts: List[str]) -> List[str]:\n ser = pd.Series(texts).fillna(\"\").astype(str)\n ser = ser.apply(_ascii_to_diacritics)\n\n # Uppercase determinatives are unwrapped, lowercase ones are converted to braces.\n ser = ser.str.replace(_DET_UPPER_RE, r\"\\1\", regex=True)\n ser = ser.str.replace(_DET_LOWER_RE, r\"{\\1}\", regex=True)\n\n ser = _normalize_gaps_vec(ser)\n ser = ser.str.translate(_CHAR_TRANS)\n ser = ser.str.replace(_SUB_X, \"\", regex=False)\n ser = ser.str.replace(_KUBABBAR_RE, \"KÙ.BABBAR\", regex=True)\n ser = ser.str.replace(_EXACT_FRAC_RE, _frac_repl, regex=True)\n ser = ser.str.replace(_FLOAT_RE, lambda m: _canon_decimal(float(m.group(1))), regex=True)\n ser = ser.str.replace(_WS_RE, \" \", regex=True).str.strip()\n return ser.tolist() ","metadata":{},"outputs":[],"execution_count":null},{"id":"0d63aecd-7666-4264-8368-7a3f1c7f30f1","cell_type":"code","source":"def clean_translation(text):\n\n # remove brackets\n text = re.sub(r\"\\[|\\]\", \"\", text)\n text = re.sub(r\"[()]\", \"\", text)\n\n # remove uncertainty markers\n text = text.replace(\"?\", \"\")\n\n # remove backslash\n text = text.replace(\"\\\\\", \"\")\n\n # keep only first word in slash alternatives\n text = re.sub(r\"\\s*/\\s*\\w+\", \"\", text)\n\n # normalize spaces\n text = re.sub(r\"\\s+\", \" \", text)\n\n return text.strip()","metadata":{},"outputs":[],"execution_count":null},{"id":"ec1d02b0","cell_type":"code","source":"INPUT_DIR = Config.INPUT_DIR\ntrain_df = pd.read_csv(f\"{INPUT_DIR}/published_texts.csv\",encoding=\"utf-8\",encoding_errors=\"ignore\")\ntest_df = pd.read_csv(f\"{INPUT_DIR}/val_200.csv\")\n# newdf = pd.read_csv(r'newtrain_data.csv')\n# train_df = pd.concat([train_df,newdf],axis=0)\n# train_df = train_df.reset_index(drop=True)","metadata":{"papermill":{"duration":0.078658,"end_time":"2026-01-07T10:22:55.620919","exception":false,"start_time":"2026-01-07T10:22:55.542261","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"8eb1b33f-744a-4d65-9adf-5a8827b80339","cell_type":"code","source":"preprocessing_text = OptimizedPreprocessor()\ntrain_df[\"transliteration\"] = preprocessing_text.preprocess_batch(train_df['transliteration'].to_list()) #train_df[\"transliteration\"].apply(lambda x:preprocess_input_text(x))\ntest_df[\"transliteration\"] = preprocessing_text.preprocess_batch(test_df['transliteration'].to_list()) #train_df[\"transliteration\"].apply(lambda x:preprocess_input_text(x))\n# train_df[\"translation\"] = train_df['translation'].apply(lambda x:clean_translation(x)) #to_list()) #train_df[\"transliteration\"].apply(lambda x:preprocess_input_text(x))\n# test_df[\"translation\"] = test_df['translation'].apply(lambda x:clean_translation(x))","metadata":{},"outputs":[],"execution_count":null},{"id":"18737801","cell_type":"code","source":"print(f\"Original Train Data: {len(train_df)} docs\")\nprint(f\"Original Train Data: {len(test_df)} docs\")","metadata":{"papermill":{"duration":0.01853,"end_time":"2026-01-07T10:22:55.651756","exception":false,"start_time":"2026-01-07T10:22:55.633226","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"ede0d431-b311-4de2-9e1c-136973b34cc7","cell_type":"code","source":"# train_df['translation'][0]","metadata":{},"outputs":[],"execution_count":null},{"id":"4c5130fe-1922-4603-9c6a-bcf2629e6077","cell_type":"code","source":"train_df['transliteration'][872]","metadata":{},"outputs":[],"execution_count":null},{"id":"aa8d52c4-feeb-47e2-826a-871f35a6b31c","cell_type":"code","source":"print(f\"Training from scratch using {Config.MODEL_NAME}...\")\ntokenizer = AutoTokenizer.from_pretrained(Config.MODEL_NAME)\nmodel = AutoModelForSeq2SeqLM.from_pretrained(Config.MODEL_NAME)\nprint(\"✓ Base model loaded successfully\")","metadata":{},"outputs":[],"execution_count":null},{"id":"6d44aded-9063-4482-84f6-4db2ad86988d","cell_type":"code","source":"model.config","metadata":{},"outputs":[],"execution_count":null},{"id":"9be9e790-5fab-4720-87b8-40a47170dec2","cell_type":"code","source":"from transformers import T5Config, T5ForConditionalGeneration","metadata":{},"outputs":[],"execution_count":null},{"id":"66684c56-2836-42cb-ba15-a51fa7c3d108","cell_type":"code","source":"config = {\n \"architectures\": [\n \"T5ForConditionalGeneration\"\n ],\n \"classifier_dropout\": 0.0,\n \"d_ff\": 3968,\n \"d_kv\": 64,\n \"d_model\": 1536,\n \"decoder_start_token_id\": 0,\n \"dense_act_fn\": \"gelu_new\",\n \"dropout_rate\": 0.1,\n \"dtype\": \"float32\",\n \"eos_token_id\": 1,\n \"feed_forward_proj\": \"gated-gelu\",\n \"gradient_checkpointing\": False,\n \"initializer_factor\": 1.0,\n \"is_encoder_decoder\": True,\n \"is_gated_act\": True,\n \"layer_norm_epsilon\": 1e-06,\n \"model_type\": \"t5\",\n \"num_decoder_layers\": 3,\n \"num_heads\": 8,\n \"num_layers\": 12,\n \"output_past\": True,\n \"pad_token_id\": 0,\n \"relative_attention_max_distance\": 128,\n \"relative_attention_num_buckets\": 32,\n \"tie_word_embeddings\": False,\n \"tokenizer_class\": \"ByT5Tokenizer\",\n \"transformers_version\": \"4.57.1\",\n \"use_cache\": True,\n \"vocab_size\": 384\n}\n# config = T5Config(**config)","metadata":{},"outputs":[],"execution_count":null},{"id":"b83038be-dba4-493d-99c2-fbef1a92125c","cell_type":"code","source":"# model = T5ForConditionalGeneration(config)","metadata":{},"outputs":[],"execution_count":null},{"id":"01caa91e-83fd-4e2b-b459-ae05efe9c772","cell_type":"code","source":"total_params = sum(p.numel() for p in model.parameters())\ntrainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n\nprint(f\"Total params: {total_params:,}\")\nprint(f\"Trainable params: {trainable_params:,}\")\nprint(f\"Trainable %: {100 * trainable_params / total_params:.4f}%\")","metadata":{},"outputs":[],"execution_count":null},{"id":"a46769a3-2791-4339-af9b-c81c5c5e5c8c","cell_type":"code","source":"texts = train_df['transliteration'].to_list()","metadata":{},"outputs":[],"execution_count":null},{"id":"13bd309f-771c-4015-a559-9e0bc9427371","cell_type":"code","source":"token_lengths = []\ntokenized_texts = []\n\nprint(\"Tokenizing dataset...\")\n\nfor text in tqdm(texts):\n\n tokens = tokenizer.encode(text, add_special_tokens=False)\n\n token_lengths.append(len(tokens))\n tokenized_texts.append(tokens)\n\ntrain_df[\"token_length\"] = token_lengths\n\n# ---------------------------------------------------\n# TOKEN STATISTICS\n# ---------------------------------------------------\n\nprint(\"\\nToken Statistics\")\nprint(\"----------------------\")\n\nprint(\"Total rows:\", len(train_df))\nprint(\"Mean length:\", np.mean(token_lengths))\nprint(\"Median length:\", np.median(token_lengths))\nprint(\"Max length:\", np.max(token_lengths))\nprint(\"95th percentile:\", np.percentile(token_lengths, 95))\n\n# histogram bins\nbins = [0,128,256,384,512,1024,2048]\n\nhist = pd.cut(token_lengths, bins).value_counts().sort_index()\n\nprint(\"\\nToken Length Distribution\")\nprint(hist)","metadata":{},"outputs":[],"execution_count":null},{"id":"6c3dfd86-2d46-4cdb-94ee-d9b630bad69a","cell_type":"code","source":"# ---------------------------------------------------\n# CHUNKING FUNCTION\n# ---------------------------------------------------\n\nMAX_CHUNK = 256 # ideal chunk size\nSPLIT_THRESHOLD = 256 # split if above this\n\ndef chunk_tokens(tokens, chunk_size=MAX_CHUNK):\n\n chunks = []\n\n if len(tokens) <= chunk_size:\n chunks.append(tokens)\n\n else:\n\n n_chunks = int(np.ceil(len(tokens) / chunk_size))\n\n split_size = int(np.ceil(len(tokens) / n_chunks))\n\n for i in range(0, len(tokens), split_size):\n\n chunk = tokens[i:i+split_size]\n\n if len(chunk) > 0:\n chunks.append(chunk)\n\n return chunks","metadata":{},"outputs":[],"execution_count":null},{"id":"cc652855-6382-4fdb-bc37-510563ea455e","cell_type":"code","source":"# ---------------------------------------------------\n# CREATE CHUNKED DATASET\n# ---------------------------------------------------\n\nchunk_rows = []\n\nprint(\"\\nChunking sequences...\")\n\nfor idx, tokens in tqdm(enumerate(tokenized_texts), total=len(tokenized_texts)):\n\n chunks = chunk_tokens(tokens)\n\n for c_id, chunk in enumerate(chunks):\n\n text_chunk = tokenizer.decode(chunk)\n\n chunk_rows.append({\n \"original_id\": idx,\n \"chunk_id\": c_id,\n \"chunk_token_length\": len(chunk),\n \"chunk_text\": text_chunk\n })\n\nchunk_df = pd.DataFrame(chunk_rows)\n\nprint(\"\\nOriginal dataset size:\", len(train_df))\nprint(\"Chunked dataset size:\", len(chunk_df))","metadata":{},"outputs":[],"execution_count":null},{"id":"c0d48c5c-6596-4a36-89bd-ce6a805a9c95","cell_type":"code","source":"texts = chunk_df['chunk_text'].to_list()","metadata":{},"outputs":[],"execution_count":null},{"id":"b8f50807-8531-4b85-9903-f1beb8680db0","cell_type":"code","source":"# chunk_df.to_csv(r'./train_data_chunked.csv', index=False)","metadata":{},"outputs":[],"execution_count":null},{"id":"2b5cb6e9-4f08-4ff5-84be-d149696fa420","cell_type":"code","source":"sentinel_tokens = [f\"\" for i in range(100)]\nsentinel_ids = tokenizer.convert_tokens_to_ids(sentinel_tokens)","metadata":{},"outputs":[],"execution_count":null},{"id":"6cca8ca6-2c08-425e-afd7-2f619d0f5886","cell_type":"code","source":"CORRUPTION_RATE = 0.12\nMEAN_SPAN_LENGTH = 10\nNUM_VARIANTS = 1 # how many corrupted samples per sentence\n\n# -----------------------------------------\n# SPAN LENGTH SAMPLING\n# -----------------------------------------\n\ndef sample_span_lengths(num_tokens, corruption_rate=0.15, mean_span_length=3):\n\n num_to_corrupt = int(num_tokens * corruption_rate)\n\n p = 1.0 / mean_span_length\n\n spans = []\n total_corrupted = 0\n\n while total_corrupted < num_to_corrupt:\n\n span_length = np.random.geometric(p)\n span_length = min(span_length, num_to_corrupt - total_corrupted)\n\n spans.append(span_length)\n total_corrupted += span_length\n\n return spans\n\n\n# -----------------------------------------\n# SPAN CORRUPTION FUNCTION\n# -----------------------------------------\n\ndef corrupt_spans(token_ids):\n\n num_tokens = len(token_ids)\n\n span_lengths = sample_span_lengths(\n num_tokens,\n corruption_rate=CORRUPTION_RATE,\n mean_span_length=MEAN_SPAN_LENGTH,\n )\n\n available_positions = list(range(num_tokens))\n span_positions = []\n\n for length in span_lengths:\n\n valid_starts = []\n\n for pos in available_positions:\n\n if pos + length <= num_tokens:\n\n span_range = set(range(pos, pos + length))\n\n if span_range.issubset(set(available_positions)):\n valid_starts.append(pos)\n\n if not valid_starts:\n continue\n\n start = random.choice(valid_starts)\n\n span_positions.append((start, length))\n\n for i in range(start, start + length):\n available_positions.remove(i)\n\n span_positions.sort(key=lambda x: x[0])\n\n corrupted_input = []\n target = []\n\n sentinel_index = 0\n current_pos = 0\n\n for start, length in span_positions:\n\n corrupted_input.extend(token_ids[current_pos:start])\n\n corrupted_input.append(sentinel_ids[sentinel_index])\n\n target.append(sentinel_ids[sentinel_index])\n target.extend(token_ids[start:start + length+10])\n\n sentinel_index += 1\n current_pos = start + length\n\n corrupted_input.extend(token_ids[current_pos:])\n\n return corrupted_input, target","metadata":{},"outputs":[],"execution_count":null},{"id":"4ac5b505-561a-4da8-bf11-88bdfdbf3aaf","cell_type":"code","source":"rows = []\n\nprint(\"Creating span corruption dataset...\")\n\nfor text in tqdm(texts):\n\n token_ids = tokenizer.encode(text, add_special_tokens=False)\n\n if len(token_ids) < 20:\n continue\n\n for _ in range(NUM_VARIANTS):\n\n corrupted_input, target = corrupt_spans(token_ids)\n\n input_text = tokenizer.decode(corrupted_input)\n target_text = tokenizer.decode(target)\n\n rows.append(\n {\n \"input_text\": input_text,\n \"target_text\": target_text\n }\n )\n\n\nspan_df = pd.DataFrame(rows)","metadata":{"scrolled":true},"outputs":[],"execution_count":null},{"id":"9089e050-f541-4228-98a8-0d96ec0def0b","cell_type":"code","source":"span_df = pd.DataFrame(rows)\n\nprint(\"Generated samples:\", len(span_df))","metadata":{},"outputs":[],"execution_count":null},{"id":"05f14289-ac6a-4928-b042-b72bc3e9a31c","cell_type":"code","source":"span_df['input_text'][2]","metadata":{},"outputs":[],"execution_count":null},{"id":"31cb6e54-a19c-497a-bea9-a65f8c96fd79","cell_type":"code","source":"span_df['target_text'][28]","metadata":{},"outputs":[],"execution_count":null},{"id":"bb901854-11ec-44df-a281-fa75502287c8","cell_type":"code","source":"import torch\nfrom torch.utils.data import Dataset\nimport pandas as pd\n\n\nclass SpanMaskDataset(Dataset):\n\n def __init__(self, df, tokenizer, max_length=256):\n\n self.df = df\n self.tokenizer = tokenizer\n self.max_length = max_length\n\n self.inputs = self.df[\"input_text\"].tolist()\n self.targets = self.df[\"target_text\"].tolist()\n\n def __len__(self):\n return len(self.inputs)\n\n def __getitem__(self, idx):\n\n input_text = self.inputs[idx]\n target_text = self.targets[idx]\n\n model_inputs = self.tokenizer(\n input_text,\n truncation=True,\n max_length=self.max_length\n )\n\n labels = self.tokenizer(\n target_text,\n truncation=True,\n max_length=self.max_length\n )\n\n model_inputs[\"labels\"] = labels[\"input_ids\"]\n\n return model_inputs","metadata":{},"outputs":[],"execution_count":null},{"id":"99503050-4cab-4949-9408-a8d461997ab1","cell_type":"code","source":"from dataclasses import dataclass\nfrom typing import Optional, Union\nimport torch\n\n\n@dataclass\nclass DataCollatorForSpanCorruption:\n\n tokenizer: any\n padding: Union[bool, str] = True\n max_length: Optional[int] = None\n\n def __call__(self, features):\n\n input_ids = [f[\"input_ids\"] for f in features]\n labels = [f[\"labels\"] for f in features]\n\n batch = self.tokenizer.pad(\n {\"input_ids\": input_ids},\n padding=True,\n return_tensors=\"pt\"\n )\n\n labels_batch = self.tokenizer.pad(\n {\"input_ids\": labels},\n padding=True,\n return_tensors=\"pt\"\n )\n\n labels = labels_batch[\"input_ids\"]\n\n labels[labels == self.tokenizer.pad_token_id] = -100\n\n batch[\"labels\"] = labels\n\n return batch","metadata":{},"outputs":[],"execution_count":null},{"id":"49f3999a-db43-4af0-8cd7-c32f180460c1","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"id":"8a37de34-f028-436f-bb20-464682267c12","cell_type":"code","source":"train_dataset = SpanMaskDataset(\n span_df,\n tokenizer,\n max_length=256\n)","metadata":{},"outputs":[],"execution_count":null},{"id":"01e33974-e666-4dcf-86ef-13fa6157c93d","cell_type":"code","source":"data_collator = DataCollatorForSpanCorruption(\n tokenizer=tokenizer\n)","metadata":{},"outputs":[],"execution_count":null},{"id":"672a422f-5489-4b23-9809-83d86db8b5b2","cell_type":"code","source":"from transformers import TrainingArguments\n\ntraining_args = TrainingArguments(\n\n output_dir=\"./pretrain_model_small\",\n\n num_train_epochs=10,\n\n per_device_train_batch_size=8,\n\n learning_rate=5e-4,\n\n warmup_steps=500,\n\n logging_steps=250,\n\n save_steps=1000,\n\n save_total_limit=1,\n\n bf16=True,\n\n gradient_accumulation_steps=2,\n\n report_to=\"none\"\n)\n\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,\n tokenizer=tokenizer,\n data_collator=data_collator\n)","metadata":{},"outputs":[],"execution_count":null},{"id":"f907a782-d34e-4845-908b-38ccb869ff00","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"id":"be197730-e826-4f8d-a739-d571b9145bbe","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"id":"c20decc8-b2ff-4b7a-9b71-ec7f5a1e1b83","cell_type":"code","source":"trainer.train()","metadata":{"scrolled":true},"outputs":[],"execution_count":null},{"id":"f0dce7dc-9e73-4af0-abfc-35928f3cb924","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"id":"620104b2-7b5c-4554-96c1-0403ee0bb27f","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"id":"14ec03d1-971c-4d61-b743-a111abb684dc","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"id":"2bcc7b0a-8393-4fc7-b8b2-4e18de8dcc71","cell_type":"code","source":"model.eval()","metadata":{},"outputs":[],"execution_count":null},{"id":"40ef783e-598d-4820-976c-faa348289cd8","cell_type":"code","source":"def predict_span(text):\n\n inputs = tokenizer(\n text,\n return_tensors=\"pt\",\n truncation=True,\n max_length=256\n )\n\n with torch.no_grad():\n outputs = model.generate(\n inputs[\"input_ids\"].to('cuda'),\n max_length=256,\n num_beams=5,\n early_stopping=True\n )\n\n prediction = tokenizer.decode(outputs[0], skip_special_tokens=False)\n\n return prediction","metadata":{},"outputs":[],"execution_count":null},{"id":"c12aa78c-400d-4723-9521-84f15bd5d708","cell_type":"code","source":"texts[162]","metadata":{},"outputs":[],"execution_count":null},{"id":"da51a1a7-4ed7-4cfb-9e36-51ca7cd2a60f","cell_type":"code","source":"print('input text : ',texts[1612])\nprint()\nprint(\"output text from model : \",predict_span(texts[161]))","metadata":{},"outputs":[],"execution_count":null},{"id":"d8600d9b-8f0e-489d-a345-e0db7c3774cb","cell_type":"code","source":"test = \"15 GÍN KÙ.BABBAR a-na PN\"\n\nprint(predict_span(test))","metadata":{},"outputs":[],"execution_count":null},{"id":"0785ce05-6975-4442-a9e7-d3b853b949db","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"id":"f55056de-f29b-4c1b-abdb-d4a6c20b032c","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"id":"6d132205-32ba-44ec-b04f-92885fdc0ed2","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"id":"f90dc680-3a79-4c17-a0c0-4b24f7492375","cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null}]}