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install -qq evaluate sacrebleu wandb ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:36:06.626535Z","iopub.execute_input":"2026-02-28T08:36:06.626786Z","iopub.status.idle":"2026-02-28T08:36:12.866017Z","shell.execute_reply.started":"2026-02-28T08:36:06.626765Z","shell.execute_reply":"2026-02-28T08:36:12.865106Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import 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 TorchAoConfig, AutoModelForSeq2SeqLM, AutoTokenizer, Seq2SeqTrainingArguments, Seq2SeqTrainer, DataCollatorForSeq2Seq\nfrom peft import LoraModel, LoraConfig","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:36:12.867962Z","iopub.execute_input":"2026-02-28T08:36:12.868665Z","iopub.status.idle":"2026-02-28T08:36:56.508904Z","shell.execute_reply.started":"2026-02-28T08:36:12.868620Z","shell.execute_reply":"2026-02-28T08:36:56.508146Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from kaggle_secrets import UserSecretsClient\nimport wandb\n\nuser_secrets = UserSecretsClient()\nwandb.login(key=user_secrets.get_secret(\"WANDB_API_KEY\"))\n\nwandb.init(\n    project=\"akkadian-lora\",\n    mode=\"online\",\n    settings=wandb.Settings(_disable_stats=True)\n)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\n\nBASE_SAVE_PATH = \"/kaggle/working/byt5_base\"\n\nmodel = AutoModelForSeq2SeqLM.from_pretrained(\n    \"google/byt5-large\",\n    # dtype=torch.bfloat16,\n    device_map=\"auto\",\n)\n\n\nmodel.save_pretrained(BASE_SAVE_PATH)\n","metadata":{"execution":{"iopub.status.busy":"2026-02-28T08:36:56.509860Z","iopub.execute_input":"2026-02-28T08:36:56.510716Z","iopub.status.idle":"2026-02-28T08:37:51.415798Z","shell.execute_reply.started":"2026-02-28T08:36:56.510690Z","shell.execute_reply":"2026-02-28T08:37:51.415159Z"},"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},{"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-large\" \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 = 15\n    LEARNING_RATE = 2e-4\n    OUTPUT_DIR = \"./byt5-akkadian-model\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:37:51.416815Z","iopub.execute_input":"2026-02-28T08:37:51.417063Z","iopub.status.idle":"2026-02-28T08:37:51.421361Z","shell.execute_reply.started":"2026-02-28T08:37:51.417041Z","shell.execute_reply":"2026-02-28T08:37:51.420714Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"config = LoraConfig(\n    task_type=\"SEQ_2_SEQ_LM\",\n    r=16,\n    lora_alpha=64,\n    target_modules=[\"q\", \"k\", \"v\", \"o\", \"wi_0\", \"wi_1\", \"wo\"],\n    lora_dropout=0.05,\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:37:51.423069Z","iopub.execute_input":"2026-02-28T08:37:51.423469Z","iopub.status.idle":"2026-02-28T08:38:02.658928Z","shell.execute_reply.started":"2026-02-28T08:37:51.423446Z","shell.execute_reply":"2026-02-28T08:38:02.657907Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"lora_model = get_peft_model(model, config)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:38:02.659884Z","iopub.execute_input":"2026-02-28T08:38:02.660184Z","iopub.status.idle":"2026-02-28T08:38:08.112695Z","shell.execute_reply.started":"2026-02-28T08:38:02.660149Z","shell.execute_reply":"2026-02-28T08:38:08.111923Z"}},"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\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:38:08.113671Z","iopub.execute_input":"2026-02-28T08:38:08.114151Z","iopub.status.idle":"2026-02-28T08:38:11.464612Z","shell.execute_reply.started":"2026-02-28T08:38:08.114118Z","shell.execute_reply":"2026-02-28T08:38:11.463383Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\ndef preprocess_function(\n    texts: List[str],\n    aggressive: bool = True\n) -> List[str]:\n    \"\"\"\n    Unified preprocessing + postprocessing pipeline.\n    Applies:\n      - Gap normalization\n      - ASCII/subscript normalization\n      - Annotation removal\n      - Repetition cleanup\n      - Fraction normalization\n      - Punctuation/whitespace cleanup\n    \"\"\"\n\n    # -----------------------------\n    # Precompiled regex patterns\n    # -----------------------------\n\n    patterns = {\n        # Input side\n        \"big_gap_input\": re.compile(r\"(\\.{3,}|…+|……)\"),\n        \"small_gap_input\": re.compile(r\"(xx+|\\s+x\\s+)\"),\n\n        # Output side\n        \"gap\": re.compile(r\"(\\[x\\]|\\(x\\)|\\bx\\b)\", re.I),\n        \"big_gap\": re.compile(r\"(\\.{3,}|…|\\[\\.+\\])\"),\n        \"annotations\": re.compile(r\"\\((fem|plur|pl|sing|singular|plural|\\?|!)\\..\\s*\\w*\\)\", re.I),\n        \"repeated_words\": re.compile(r\"\\b(\\w+)(?:\\s+\\1\\b)+\"),\n        \"whitespace\": re.compile(r\"\\s+\"),\n        \"punct_space\": re.compile(r\"\\s+([.,:])\"),\n        \"repeated_punct\": re.compile(r\"([.,])\\1+\"),\n    }\n\n    subscript_trans = str.maketrans(\"₀₁₂₃₄₅₆₇₈₉\", \"0123456789\")\n    special_chars_trans = str.maketrans(\"ḫḪ\", \"hH\")\n    forbidden_chars = '!?()\"——<>⌈⌋⌊[]+ʾ/;'\n    forbidden_trans = str.maketrans(\"\", \"\", forbidden_chars)\n\n    # -----------------------------\n    # Convert to pandas Series\n    # -----------------------------\n\n    s = pd.Series(texts).fillna(\"\").astype(str)\n\n    # -----------------------------\n    # 1️⃣ INPUT CLEANING\n    # -----------------------------\n\n    s = s.str.replace(patterns[\"big_gap_input\"], \"<big_gap>\", regex=True)\n    s = s.str.replace(patterns[\"small_gap_input\"], \"<gap>\", regex=True)\n\n    # -----------------------------\n    # 2️⃣ BASIC NORMALIZATION\n    # -----------------------------\n\n    s = s.str.translate(special_chars_trans)\n    s = s.str.translate(subscript_trans)\n    s = s.str.replace(patterns[\"whitespace\"], \" \", regex=True)\n    s = s.str.strip()\n\n    # -----------------------------\n    # 3️⃣ AGGRESSIVE CLEANING\n    # -----------------------------\n\n    if aggressive:\n\n        # Normalize gap variants again\n        s = s.str.replace(patterns[\"gap\"], \"<gap>\", regex=True)\n        s = s.str.replace(patterns[\"big_gap\"], \"<big_gap>\", regex=True)\n\n        # Merge adjacent gaps\n        s = s.str.replace(\"<gap> <gap>\", \"<big_gap>\", regex=False)\n        s = s.str.replace(\"<big_gap> <big_gap>\", \"<big_gap>\", regex=False)\n\n        # Remove grammatical annotations\n        s = s.str.replace(patterns[\"annotations\"], \"\", regex=True)\n\n        # Protect gap tokens\n        s = s.str.replace(\"<gap>\", \"\\x00GAP\\x00\", regex=False)\n        s = s.str.replace(\"<big_gap>\", \"\\x00BIG\\x00\", regex=False)\n\n        # Remove forbidden characters\n        s = s.str.translate(forbidden_trans)\n\n        # Restore gap tokens\n        s = s.str.replace(\"\\x00GAP\\x00\", \" <gap> \", regex=False)\n        s = s.str.replace(\"\\x00BIG\\x00\", \" <big_gap> \", regex=False)\n\n        # Fraction normalization\n        s = s.str.replace(r\"(\\d+)\\.5\\b\", r\"\\1½\", regex=True)\n        s = s.str.replace(r\"\\b0\\.5\\b\", \"½\", regex=True)\n        s = s.str.replace(r\"(\\d+)\\.25\\b\", r\"\\1¼\", regex=True)\n        s = s.str.replace(r\"\\b0\\.25\\b\", \"¼\", regex=True)\n        s = s.str.replace(r\"(\\d+)\\.75\\b\", r\"\\1¾\", regex=True)\n        s = s.str.replace(r\"\\b0\\.75\\b\", \"¾\", regex=True)\n\n        # Remove repeated words\n        s = s.str.replace(patterns[\"repeated_words\"], r\"\\1\", regex=True)\n\n        # Remove repeated n-grams (4 → 2)\n        for n in range(4, 1, -1):\n            pattern = r\"\\b((?:\\w+\\s+){\" + str(n - 1) + r\"}\\w+)(?:\\s+\\1\\b)+\"\n            s = s.str.replace(pattern, r\"\\1\", regex=True)\n\n        # Fix punctuation spacing\n        s = s.str.replace(patterns[\"punct_space\"], r\"\\1\", regex=True)\n\n        # Remove repeated punctuation\n        s = s.str.replace(patterns[\"repeated_punct\"], r\"\\1\", regex=True)\n\n        # Final whitespace cleanup\n        s = s.str.replace(patterns[\"whitespace\"], \" \", regex=True)\n        s = s.str.strip().str.strip(\"-\").str.strip()\n\n    return s.tolist()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:38:11.465797Z","iopub.execute_input":"2026-02-28T08:38:11.466109Z","iopub.status.idle":"2026-02-28T08:38:15.959988Z","shell.execute_reply.started":"2026-02-28T08:38:11.466073Z","shell.execute_reply":"2026-02-28T08:38:15.959127Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import re\nimport pandas as pd\nfrom typing import List\n\n# -------------------------------------------------\n# Unified Cleaning Function (collapsed version)\n# -------------------------------------------------\n\ndef clean_akkadian_batch(texts: List[str], aggressive: bool = True) -> List[str]:\n\n    patterns = {\n        \"big_gap_input\": re.compile(r\"(\\.{3,}|…+|……)\"),\n        \"small_gap_input\": re.compile(r\"(xx+|\\s+x\\s+)\"),\n        \"gap\": re.compile(r\"(\\[x\\]|\\(x\\)|\\bx\\b)\", re.I),\n        \"big_gap\": re.compile(r\"(\\.{3,}|…|\\[\\.+\\])\"),\n        \"annotations\": re.compile(r\"\\((fem|plur|pl|sing|singular|plural|\\?|!)\\..\\s*\\w*\\)\", re.I),\n        \"repeated_words\": re.compile(r\"\\b(\\w+)(?:\\s+\\1\\b)+\"),\n        \"whitespace\": re.compile(r\"\\s+\"),\n        \"punct_space\": re.compile(r\"\\s+([.,:])\"),\n        \"repeated_punct\": re.compile(r\"([.,])\\1+\"),\n    }\n\n    subscript_trans = str.maketrans(\"₀₁₂₃₄₅₆₇₈₉\", \"0123456789\")\n    special_chars_trans = str.maketrans(\"ḫḪ\", \"hH\")\n    forbidden_chars = '!?()\"——<>⌈⌋⌊[]+ʾ/;'\n    forbidden_trans = str.maketrans(\"\", \"\", forbidden_chars)\n\n    s = pd.Series(texts).fillna(\"\").astype(str)\n\n    # Input normalization\n    s = s.str.replace(patterns[\"big_gap_input\"], \"<big_gap>\", regex=True)\n    s = s.str.replace(patterns[\"small_gap_input\"], \"<gap>\", regex=True)\n\n    # Basic normalization\n    s = s.str.translate(special_chars_trans)\n    s = s.str.translate(subscript_trans)\n    s = s.str.replace(patterns[\"whitespace\"], \" \", regex=True)\n    s = s.str.strip()\n\n    if aggressive:\n\n        s = s.str.replace(patterns[\"gap\"], \"<gap>\", regex=True)\n        s = s.str.replace(patterns[\"big_gap\"], \"<big_gap>\", regex=True)\n\n        s = s.str.replace(\"<gap> <gap>\", \"<big_gap>\", regex=False)\n        s = s.str.replace(\"<big_gap> <big_gap>\", \"<big_gap>\", regex=False)\n\n        s = s.str.replace(patterns[\"annotations\"], \"\", regex=True)\n\n        s = s.str.replace(\"<gap>\", \"\\x00GAP\\x00\", regex=False)\n        s = s.str.replace(\"<big_gap>\", \"\\x00BIG\\x00\", regex=False)\n\n        s = s.str.translate(forbidden_trans)\n\n        s = s.str.replace(\"\\x00GAP\\x00\", \" <gap> \", regex=False)\n        s = s.str.replace(\"\\x00BIG\\x00\", \" <big_gap> \", regex=False)\n\n        s = s.str.replace(r\"(\\d+)\\.5\\b\", r\"\\1½\", regex=True)\n        s = s.str.replace(r\"\\b0\\.5\\b\", \"½\", regex=True)\n        s = s.str.replace(r\"(\\d+)\\.25\\b\", r\"\\1¼\", regex=True)\n        s = s.str.replace(r\"\\b0\\.25\\b\", \"¼\", regex=True)\n        s = s.str.replace(r\"(\\d+)\\.75\\b\", r\"\\1¾\", regex=True)\n        s = s.str.replace(r\"\\b0\\.75\\b\", \"¾\", regex=True)\n\n        s = s.str.replace(patterns[\"repeated_words\"], r\"\\1\", regex=True)\n\n        for n in range(4, 1, -1):\n            pattern = r\"\\b((?:\\w+\\s+){\" + str(n - 1) + r\"}\\w+)(?:\\s+\\1\\b)+\"\n            s = s.str.replace(pattern, r\"\\1\", regex=True)\n\n        s = s.str.replace(patterns[\"punct_space\"], r\"\\1\", regex=True)\n        s = s.str.replace(patterns[\"repeated_punct\"], r\"\\1\", regex=True)\n        s = s.str.replace(patterns[\"whitespace\"], \" \", regex=True)\n        s = s.str.strip().str.strip(\"-\").str.strip()\n\n    return s.tolist()\n\n\n# -------------------------------------------------\n# Sentence Aligner with Cleaning Integrated\n# -------------------------------------------------\n\ndef simple_sentence_aligner(df):\n\n    aligned_data = []\n\n    for _, row in df.iterrows():\n\n        # Clean entire document pair first\n        src, tgt = clean_akkadian_batch(\n            [row['transliteration'], row['translation']],\n            aggressive=True\n        )\n\n        # Split English by sentence-ending punctuation\n        tgt_sents = [\n            t.strip()\n            for t in re.split(r'(?<=[.!?])\\s+', tgt)\n            if t.strip()\n        ]\n\n        # Split Akkadian by newline\n        src_lines = [\n            s.strip()\n            for s in src.split('\\n')\n            if s.strip()\n        ]\n\n        # 1-to-1 case\n        if len(tgt_sents) > 1 and len(tgt_sents) == len(src_lines):\n\n            cleaned_pairs = clean_akkadian_batch(\n                src_lines + tgt_sents,\n                aggressive=True\n            )\n\n            src_cleaned = cleaned_pairs[:len(src_lines)]\n            tgt_cleaned = cleaned_pairs[len(src_lines):]\n\n            for s, t in zip(src_cleaned, tgt_cleaned):\n                if len(s) > 3 and len(t) > 3:\n                    aligned_data.append({\n                        'transliteration': s,\n                        'translation': t\n                    })\n\n        else:\n            # Fallback (cleaned full pair)\n            if len(src) > 3 and len(tgt) > 3:\n                aligned_data.append({\n                    'transliteration': src,\n                    'translation': tgt\n                })\n\n    return pd.DataFrame(aligned_data)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:38:15.961106Z","iopub.execute_input":"2026-02-28T08:38:15.961447Z","iopub.status.idle":"2026-02-28T08:38:22.140344Z","shell.execute_reply.started":"2026-02-28T08:38:15.961410Z","shell.execute_reply":"2026-02-28T08:38:22.139683Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_expanded = simple_sentence_aligner(train_df)\nprint(f\"Expanded Train Data: {len(train_expanded)} sentences (Alignment applied)\")\ndataset = Dataset.from_pandas(train_expanded)\nsplit_datasets = dataset.train_test_split(test_size=0.1, seed=42)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:38:22.141299Z","iopub.execute_input":"2026-02-28T08:38:22.141624Z","iopub.status.idle":"2026-02-28T08:38:30.887322Z","shell.execute_reply.started":"2026-02-28T08:38:22.141598Z","shell.execute_reply":"2026-02-28T08:38:30.886696Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"tokenizer = AutoTokenizer.from_pretrained(Config.MODEL_NAME)\ntokenizer.save_pretrained(BASE_SAVE_PATH)\n\n# Fix the corresponding section in dpc-starter-train.\nPREFIX = \"translate Akkadian to English: \"\n\ndef preprocess_function(examples):\n    inputs = [PREFIX + str(ex) for ex in examples[\"transliteration\"]]\n    targets = [str(ex) for ex in examples[\"translation\"]]\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\ntokenized_train = split_datasets[\"train\"].map(preprocess_function, batched=True)\ntokenized_val = split_datasets[\"test\"].map(preprocess_function, batched=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:38:30.888141Z","iopub.execute_input":"2026-02-28T08:38:30.888389Z","iopub.status.idle":"2026-02-28T08:38:32.932054Z","shell.execute_reply.started":"2026-02-28T08:38:30.888360Z","shell.execute_reply":"2026-02-28T08:38:32.931368Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import evaluate\nimport torch\nimport gc","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:38:32.933102Z","iopub.execute_input":"2026-02-28T08:38:32.933781Z","iopub.status.idle":"2026-02-28T08:38:33.319269Z","shell.execute_reply.started":"2026-02-28T08:38:32.933754Z","shell.execute_reply":"2026-02-28T08:38:33.318702Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"gc.collect()\ntorch.cuda.empty_cache()\n# model = AutoModelForSeq2SeqLM.from_pretrained(Config.MODEL_NAME)\ndata_collator = DataCollatorForSeq2Seq(tokenizer, model=lora_model)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:38:33.320111Z","iopub.execute_input":"2026-02-28T08:38:33.320387Z","iopub.status.idle":"2026-02-28T08:38:33.730860Z","shell.execute_reply.started":"2026-02-28T08:38:33.320343Z","shell.execute_reply":"2026-02-28T08:38:33.730014Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from transformers import EarlyStoppingCallback","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:38:33.733589Z","iopub.execute_input":"2026-02-28T08:38:33.734029Z","iopub.status.idle":"2026-02-28T08:38:33.747819Z","shell.execute_reply.started":"2026-02-28T08:38:33.733999Z","shell.execute_reply":"2026-02-28T08:38:33.746940Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"metric = evaluate.load(\"chrf\")\nmodel.compile()\ndef compute_metrics(eval_preds):\n    preds, labels = eval_preds\n    \n    if isinstance(preds, tuple):\n        preds = preds[0]\n\n    preds = np.where(preds < 0, tokenizer.pad_token_id, preds)\n    preds = np.where(preds >= tokenizer.vocab_size, tokenizer.pad_token_id, preds)\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    decoded_preds = [pred.strip() for pred in decoded_preds]\n    decoded_labels = [label.strip() for label in decoded_labels]\n\n    formatted_labels = [[label] for label in decoded_labels]\n\n    result = metric.compute(\n        predictions=decoded_preds,\n        references=formatted_labels\n    )\n\n    return {\"chrf\": result[\"score\"]}\n    \n\nargs = Seq2SeqTrainingArguments(\n    output_dir=Config.OUTPUT_DIR,\n\n    eval_strategy=\"epoch\",\n    save_strategy=\"epoch\",\n\n    learning_rate=Config.LEARNING_RATE,\n\n    per_device_train_batch_size=2,\n    per_device_eval_batch_size=2,\n    gradient_accumulation_steps=8,\n\n    weight_decay=0.01,\n    num_train_epochs=Config.EPOCHS,\n    save_total_limit=2,\n\n    predict_with_generate=True,\n    generation_num_beams=4,\n    generation_max_length=256,  # reduced\n\n    logging_strategy=\"steps\",\n    logging_steps=100,          # reduced frequency\n\n    report_to=\"wandb\",          # when ready\n\n    load_best_model_at_end=True,\n    metric_for_best_model=\"chrf\",\n    greater_is_better=True,\n)\n\ntrainer = Seq2SeqTrainer(\n    model=lora_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=[EarlyStoppingCallback(early_stopping_patience=2)]\n)\n\nprint(\"Starting Training (FP32 mode)...\")\ntrainer.train()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-02-28T08:38:33.748946Z","iopub.execute_input":"2026-02-28T08:38:33.749669Z","execution_failed":"2026-02-28T08:39:23.038Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"wandb.finish()","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}