{ "cells": [ { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 24.0 -> 26.1.1\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "%pip install -q transformers datasets accelerate evaluate seqeval ftfy nltk pandas numpy optuna matplotlib\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: sentencepiece in d:\\vsc\\nlp_aol\\.venv\\lib\\site-packages (0.2.1)\n", "Requirement already satisfied: tiktoken in d:\\vsc\\nlp_aol\\.venv\\lib\\site-packages (0.13.0)\n", "Requirement already satisfied: regex in d:\\vsc\\nlp_aol\\.venv\\lib\\site-packages (from tiktoken) (2026.5.9)\n", "Requirement already satisfied: requests in d:\\vsc\\nlp_aol\\.venv\\lib\\site-packages (from tiktoken) (2.34.2)\n", "Requirement already satisfied: charset_normalizer<4,>=2 in d:\\vsc\\nlp_aol\\.venv\\lib\\site-packages (from requests->tiktoken) (2.1.1)\n", "Requirement already satisfied: idna<4,>=2.5 in d:\\vsc\\nlp_aol\\.venv\\lib\\site-packages (from requests->tiktoken) (3.4)\n", "Requirement already satisfied: urllib3<3,>=1.26 in d:\\vsc\\nlp_aol\\.venv\\lib\\site-packages (from requests->tiktoken) (1.26.13)\n", "Requirement already satisfied: certifi>=2023.5.7 in d:\\vsc\\nlp_aol\\.venv\\lib\\site-packages (from requests->tiktoken) (2026.5.20)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 24.0 -> 26.1.1\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "%pip install sentencepiece tiktoken\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using device: cuda\n" ] } ], "source": [ "import os\n", "import random\n", "import pandas as pd\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import glob\n", "import shutil\n", "import nltk\n", "import ftfy\n", "from datasets import Dataset\n", "from nltk.tokenize import sent_tokenize\n", "from transformers import (\n", " AutoTokenizer, \n", " AutoModelForTokenClassification,\n", " BertForTokenClassification,\n", " TrainingArguments, \n", " Trainer,\n", " DataCollatorForTokenClassification,\n", " EarlyStoppingCallback,\n", " TrainerCallback,\n", " PrinterCallback\n", ")\n", "import evaluate\n", "import optuna\n", "\n", "os.environ[\"HF_HUB_DISABLE_PROGRESS_BARS\"] = \"1\"\n", "from transformers import logging as tf_logging\n", "tf_logging.set_verbosity_error()\n", "tf_logging.disable_progress_bar()\n", "\n", "from datasets import logging as ds_logging\n", "ds_logging.set_verbosity_error()\n", "ds_logging.disable_progress_bar()\n", "\n", "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "print(f\"Using device: {device}\")\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading EE21/ToS-Summaries...\n", "Loading CodeHima/TOS_Dataset...\n", "Data loaded successfully!\n" ] } ], "source": [ "print(\"Loading EE21/ToS-Summaries...\")\n", "df_summaries = pd.read_json(\"hf://datasets/EE21/ToS-Summaries/dataset.json\", lines=True)\n", "\n", "print(\"Loading CodeHima/TOS_Dataset...\")\n", "splits = {'train': 'data/train-00000-of-00001.parquet'}\n", "df_codehima = pd.read_parquet(\"hf://datasets/CodeHima/TOS_Dataset/\" + splits[\"train\"])\n", "\n", "print(\"Data loaded successfully!\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parsing CodeHima...\n", " CodeHima: 4365 neutral, 1013 gotcha (wrapped in context)\n", "Parsing EE21...\n", " EE21: 847 gotcha, 54 neutral\n", "Loading synthetic data from corpus/synthetic_gotchas.json...\n", "Adding 3479 synthetic training samples exclusively to the training split.\n", "Merged training split size: 8816 samples (Original: 5337, Synthetic: 3479)\n", "Parsed dataset: train=8816, test=942\n" ] } ], "source": [ "try:\n", " nltk.data.find('tokenizers/punkt_tab')\n", "except LookupError:\n", " nltk.download('punkt_tab', quiet=True)\n", "\n", "def parse_all_datasets(df_codehima):\n", " standardized_data = []\n", "\n", " print(\"Parsing CodeHima...\")\n", " neutral_sentences = []\n", " gotcha_sentences = []\n", " for _, row in df_codehima.iterrows():\n", " sentence = str(row['sentence'])\n", " if row['unfairness_level'] in ['potentially_unfair', 'clearly_unfair']:\n", " gotcha_sentences.append(sentence)\n", " else:\n", " neutral_sentences.append(sentence)\n", " standardized_data.append({\"text\": sentence, \"gotchas\": []})\n", "\n", " random.seed(42)\n", " for gotcha_sent in gotcha_sentences:\n", " context_sent = random.choice(neutral_sentences)\n", " combined_text = context_sent + ' ' + gotcha_sent\n", " standardized_data.append({\"text\": combined_text, \"gotchas\": [gotcha_sent]})\n", " print(f\" CodeHima: {len(neutral_sentences)} neutral, {len(gotcha_sentences)} gotcha (wrapped in context)\")\n", "\n", " return Dataset.from_list(standardized_data)\n", "\n", "raw_original_dataset = parse_all_datasets(df_codehima)\n", "\n", "dataset_splits = raw_original_dataset.train_test_split(test_size=0.15, seed=42)\n", "\n", "synthetic_path = \"corpus/synthetic_gotchas.json\"\n", "if os.path.exists(synthetic_path):\n", " import json\n", " from datasets import Dataset, concatenate_datasets\n", " print(f\"Loading synthetic data from {synthetic_path}...\")\n", " try:\n", " with open(synthetic_path, \"r\", encoding=\"utf-8\") as f:\n", " synthetic_data = json.load(f)\n", " print(f\"Adding {len(synthetic_data)} synthetic training samples exclusively to the training split.\")\n", "\n", " synthetic_dataset = Dataset.from_list(synthetic_data)\n", " original_train = dataset_splits[\"train\"]\n", " dataset_splits[\"train\"] = concatenate_datasets([original_train, synthetic_dataset])\n", " print(f\"Merged training split size: {len(dataset_splits['train'])} samples (Original: {len(original_train)}, Synthetic: {len(synthetic_dataset)})\")\n", " except Exception as e:\n", " print(f\"Error loading or merging synthetic data: {e}\")\n", "else:\n", " print(\"No synthetic gotchas found. Training on baseline splits.\")\n", "\n", "print(f\"Parsed dataset: train={len(dataset_splits['train'])}, test={len(dataset_splits['test'])}\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "label2id = {'O': 0, 'B-RISK': 1, 'I-RISK': 2}\n", "id2label = {0: 'O', 1: 'B-RISK', 2: 'I-RISK'}\n", "\n", "def tokenize_and_align_labels(example, tokenizer):\n", " text = example['text']\n", " gotchas = example['gotchas']\n", "\n", " tokenized_inputs = tokenizer(\n", " text, \n", " truncation=True, \n", " max_length=512, \n", " return_offsets_mapping=True\n", " )\n", "\n", " offsets = tokenized_inputs[\"offset_mapping\"]\n", " labels = [label2id['O']] * len(offsets)\n", "\n", " for gotcha in gotchas:\n", " start_char = text.find(gotcha)\n", " if start_char == -1: continue\n", " end_char = start_char + len(gotcha)\n", "\n", " gotcha_started = False\n", " for idx, (start, end) in enumerate(offsets):\n", " if start == end: \n", " labels[idx] = -100\n", " continue\n", "\n", " if start >= start_char and start < end_char:\n", " if not gotcha_started:\n", " labels[idx] = label2id['B-RISK']\n", " gotcha_started = True\n", " else:\n", " labels[idx] = label2id['I-RISK']\n", "\n", " tokenized_inputs[\"labels\"] = labels\n", " del tokenized_inputs[\"offset_mapping\"]\n", " return tokenized_inputs" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "\n", "TOKENIZER_MAP = {\n", " \"prajjwal1/bert-tiny\": \"google-bert/bert-base-uncased\",\n", " \"prajjwal1/bert-mini\": \"google-bert/bert-base-uncased\",\n", "}\n", "\n", "MODEL_CLASS_MAP = {\n", " \"prajjwal1/bert-tiny\": BertForTokenClassification,\n", " \"prajjwal1/bert-mini\": BertForTokenClassification,\n", "}\n", "\n", "MODEL_ARCH_MAP = {\n", " \"google/electra-small-discriminator\": {\"backbone_attr\": \"electra\", \"max_layers\": 12},\n", " \"prajjwal1/bert-tiny\": {\"backbone_attr\": \"bert\", \"max_layers\": 2},\n", " \"prajjwal1/bert-mini\": {\"backbone_attr\": \"bert\", \"max_layers\": 4},\n", " \"huawei-noah/TinyBERT_General_4L_312D\": {\"backbone_attr\": \"bert\", \"max_layers\": 4},\n", "}\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "metric = evaluate.load(\"seqeval\")\n", "\n", "class TrialMetricsCallback(TrainerCallback):\n", " \"\"\"Collects eval metrics per epoch per trial. Call display() after search to render tables.\"\"\"\n", " def __init__(self):\n", " self.all_trials = {}\n", " self._trial_num = 0\n", " self._current = []\n", "\n", " def on_log(self, args, state, control, logs=None, **kwargs):\n", " if logs and \"eval_loss\" in logs:\n", " self._current.append({\n", " \"Epoch\": int(state.epoch) if state.epoch == int(state.epoch) else round(state.epoch, 2),\n", " \"Loss\": round(logs.get(\"eval_loss\", 0), 4),\n", " \"Precision\": round(logs.get(\"eval_precision\", 0), 4),\n", " \"Recall\": round(logs.get(\"eval_recall\", 0), 4),\n", " \"F1\": round(logs.get(\"eval_f1\", 0), 4),\n", " \"Accuracy\": round(logs.get(\"eval_accuracy\", 0), 4),\n", " })\n", "\n", " def on_train_end(self, args, state, control, **kwargs):\n", " if self._current:\n", " self.all_trials[self._trial_num] = self._current\n", " self._trial_num += 1\n", " self._current = []\n", "\n", " def display(self, label_prefix=\"Trial\"):\n", " from IPython.display import display as ipy_display, HTML\n", " for trial_num, metrics in sorted(self.all_trials.items()):\n", " df = pd.DataFrame(metrics)\n", " if not df.empty:\n", " best_idx = df[\"F1\"].idxmax()\n", " best_f1 = df.loc[best_idx, \"F1\"]\n", " best_epoch = df.loc[best_idx, \"Epoch\"]\n", " ipy_display(HTML(f\"\\u250c\\u2500 {label_prefix} {trial_num + 1} \\u2500 Best F1: {best_f1:.4f} (Epoch {int(best_epoch)})\"))\n", " ipy_display(df)\n", "\n", " def reset(self):\n", " self.all_trials = {}\n", " self._trial_num = 0\n", " self._current = []\n", "\n", "def compute_metrics(p):\n", " predictions, labels = p\n", " predictions = np.argmax(predictions, axis=2)\n", "\n", " true_predictions = [\n", " [id2label[p] for (p, l) in zip(prediction, label) if l != -100]\n", " for prediction, label in zip(predictions, labels)\n", " ]\n", " true_labels = [\n", " [id2label[l] for (p, l) in zip(prediction, label) if l != -100]\n", " for prediction, label in zip(predictions, labels)\n", " ]\n", " results = metric.compute(predictions=true_predictions, references=true_labels)\n", " return {\n", " \"precision\": results[\"overall_precision\"],\n", " \"recall\": results[\"overall_recall\"],\n", " \"f1\": results[\"overall_f1\"],\n", " \"accuracy\": results[\"overall_accuracy\"],\n", " }\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "class WeightedLossTrainer(Trainer):\n", " def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):\n", " labels = inputs.get(\"labels\")\n", " outputs = model(**inputs)\n", " logits = outputs.get(\"logits\")\n", " device = logits.device\n", "\n", " loss_fct = torch.nn.CrossEntropyLoss(\n", " weight=torch.tensor([0.38, 4.70, 1.00], device=device)\n", " )\n", "\n", " if labels is not None:\n", " active_loss = inputs.get(\"attention_mask\").view(-1) == 1\n", " active_logits = logits.view(-1, self.model.config.num_labels)\n", " active_labels = torch.where(\n", " active_loss, labels.view(-1),\n", " torch.tensor(-100, device=device)\n", " )\n", " loss = loss_fct(active_logits, active_labels)\n", " else:\n", " loss = outputs.loss\n", "\n", " return (loss, outputs) if return_outputs else loss\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "\n", "def freeze_layers(model, model_checkpoint, num_frozen_layers):\n", " \"\"\"Dynamically freeze embeddings + the specified number of bottom encoder layers.\"\"\"\n", " arch = MODEL_ARCH_MAP.get(model_checkpoint)\n", " if arch is None:\n", " return\n", "\n", " backbone = getattr(model, arch[\"backbone_attr\"], None)\n", " if backbone is None:\n", " return\n", "\n", " for param in backbone.embeddings.parameters():\n", " param.requires_grad = False\n", "\n", " if hasattr(backbone, \"encoder\") and hasattr(backbone.encoder, \"layer\"):\n", " actual_layers = len(backbone.encoder.layer)\n", " num_to_freeze = min(num_frozen_layers, actual_layers)\n", " for i in range(num_to_freeze):\n", " for param in backbone.encoder.layer[i].parameters():\n", " param.requires_grad = False\n", "\n", "def optuna_train_model(model_checkpoint, save_name, n_trials=10, num_epochs=10):\n", " \"\"\"Run Optuna hyperparameter search, then retrain with best hyperparameters.\"\"\"\n", " print(f\"\\n{'='*60}\")\n", " print(f\" OPTUNA SEARCH: {model_checkpoint} ({n_trials} trials, {num_epochs} epochs each)\")\n", " print(f\"{'='*60}\")\n", "\n", " output_dir = f\"./gotcha-extractor-model/{save_name}\"\n", " search_dir = f\"./gotcha-extractor-model/{save_name}-optuna-search\"\n", "\n", " tok_name = TOKENIZER_MAP.get(model_checkpoint, model_checkpoint)\n", " model_cls = MODEL_CLASS_MAP.get(model_checkpoint, AutoModelForTokenClassification)\n", " tokenizer = AutoTokenizer.from_pretrained(\n", " tok_name, add_prefix_space=\"roberta\" in model_checkpoint\n", " )\n", "\n", " tokenized_train = dataset_splits[\"train\"].map(\n", " lambda x: tokenize_and_align_labels(x, tokenizer),\n", " remove_columns=dataset_splits[\"train\"].column_names\n", " )\n", " tokenized_test = dataset_splits[\"test\"].map(\n", " lambda x: tokenize_and_align_labels(x, tokenizer),\n", " remove_columns=dataset_splits[\"test\"].column_names\n", " )\n", " data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)\n", "\n", " arch = MODEL_ARCH_MAP.get(model_checkpoint)\n", " max_freezable = arch[\"max_layers\"] if arch else 0\n", "\n", " def model_init(trial=None):\n", " model = model_cls.from_pretrained(\n", " model_checkpoint,\n", " num_labels=len(label2id),\n", " id2label=id2label,\n", " label2id=label2id,\n", " ignore_mismatched_sizes=True\n", " ).to(device)\n", "\n", " if trial is not None and max_freezable > 0:\n", " num_frozen = trial.suggest_int(\"num_frozen_layers\", 0, max_freezable)\n", " freeze_layers(model, model_checkpoint, num_frozen)\n", " print(f\" [Trial {trial.number}] Freezing embeddings + bottom {num_frozen}/{max_freezable} layers\")\n", " elif trial is not None:\n", " freeze_layers(model, model_checkpoint, 0)\n", " print(f\" [Trial {trial.number}] Freezing embeddings only\")\n", "\n", " trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", " total = sum(p.numel() for p in model.parameters())\n", " print(f\" Trainable: {trainable:,} / {total:,}\")\n", " return model\n", "\n", " def hp_space(trial):\n", " return {\n", " \"learning_rate\": trial.suggest_float(\"learning_rate\", 1e-5, 8e-5, log=True),\n", " \"weight_decay\": trial.suggest_float(\"weight_decay\", 0.0, 0.15),\n", " \"per_device_train_batch_size\": trial.suggest_categorical(\"per_device_train_batch_size\", [8, 16]),\n", " \"warmup_ratio\": trial.suggest_float(\"warmup_ratio\", 0.05, 0.25),\n", " }\n", "\n", " metrics_cb = TrialMetricsCallback()\n", " search_args = TrainingArguments(\n", " output_dir=search_dir,\n", " eval_strategy=\"epoch\",\n", " learning_rate=5e-5,\n", " per_device_train_batch_size=8,\n", " per_device_eval_batch_size=8,\n", " num_train_epochs=num_epochs,\n", " weight_decay=0.01,\n", " logging_strategy=\"no\",\n", " disable_tqdm=True,\n", " save_strategy=\"epoch\",\n", " load_best_model_at_end=True,\n", " metric_for_best_model=\"f1\",\n", " greater_is_better=True,\n", " save_total_limit=1,\n", " warmup_ratio=0.15,\n", " report_to=\"none\",\n", " )\n", "\n", " trainer = WeightedLossTrainer(\n", " model_init=model_init,\n", " args=search_args,\n", " train_dataset=tokenized_train,\n", " eval_dataset=tokenized_test,\n", " processing_class=tokenizer,\n", " data_collator=data_collator,\n", " compute_metrics=compute_metrics,\n", " callbacks=[metrics_cb],\n", " )\n", " trainer.remove_callback(PrinterCallback)\n", "\n", " best_run = trainer.hyperparameter_search(\n", " direction=\"maximize\",\n", " backend=\"optuna\",\n", " hp_space=hp_space,\n", " n_trials=n_trials,\n", " compute_objective=lambda metrics: metrics[\"eval_f1\"],\n", " )\n", " metrics_cb.display(label_prefix=\"Trial\")\n", "\n", " import copy\n", " search_history = copy.deepcopy(metrics_cb.all_trials)\n", "\n", " print(f\"\\n{'='*60}\")\n", " print(f\" BEST HYPERPARAMETERS FOUND\")\n", " print(f\"{'='*60}\")\n", " for key, value in best_run.hyperparameters.items():\n", " print(f\" {key}: {value}\")\n", " print(f\" Best F1: {best_run.objective:.4f}\")\n", "\n", " print(f\"\\n>>> Final training with best hyperparameters <<<\")\n", "\n", " best_hp = best_run.hyperparameters\n", " best_frozen = best_hp.pop(\"num_frozen_layers\", max_freezable)\n", "\n", " final_model = model_cls.from_pretrained(\n", " model_checkpoint,\n", " num_labels=len(label2id),\n", " id2label=id2label,\n", " label2id=label2id,\n", " ignore_mismatched_sizes=True\n", " ).to(device)\n", " freeze_layers(final_model, model_checkpoint, best_frozen)\n", "\n", " trainable = sum(p.numel() for p in final_model.parameters() if p.requires_grad)\n", " total = sum(p.numel() for p in final_model.parameters())\n", " print(f\"Final model \\u2014 Frozen layers: {best_frozen}, Trainable: {trainable:,} / {total:,}\")\n", "\n", " metrics_cb.reset()\n", " final_args = TrainingArguments(\n", " output_dir=output_dir,\n", " eval_strategy=\"epoch\",\n", " learning_rate=best_hp.get(\"learning_rate\", 5e-5),\n", " per_device_train_batch_size=best_hp.get(\"per_device_train_batch_size\", 8),\n", " per_device_eval_batch_size=8,\n", " num_train_epochs=num_epochs,\n", " weight_decay=best_hp.get(\"weight_decay\", 0.01),\n", " logging_strategy=\"no\",\n", " disable_tqdm=True,\n", " save_strategy=\"epoch\",\n", " load_best_model_at_end=True,\n", " metric_for_best_model=\"f1\",\n", " greater_is_better=True,\n", " save_total_limit=1,\n", " warmup_ratio=best_hp.get(\"warmup_ratio\", 0.15),\n", " )\n", "\n", " final_trainer = WeightedLossTrainer(\n", " model=final_model,\n", " args=final_args,\n", " train_dataset=tokenized_train,\n", " eval_dataset=tokenized_test,\n", " processing_class=tokenizer,\n", " data_collator=data_collator,\n", " compute_metrics=compute_metrics,\n", " callbacks=[metrics_cb],\n", " )\n", " final_trainer.remove_callback(PrinterCallback)\n", "\n", " final_trainer.train()\n", " metrics_cb.display(label_prefix=\"Final Training Epoch\")\n", "\n", " final_history = copy.deepcopy(metrics_cb.all_trials.get(0, []))\n", "\n", " final_trainer.save_model(output_dir)\n", " tokenizer.save_pretrained(output_dir)\n", "\n", " try:\n", " import json\n", " import os\n", "\n", " def format_history(all_trials_dict):\n", " formatted = {}\n", " for t_num, epochs_list in all_trials_dict.items():\n", " formatted[str(t_num + 1)] = {\n", " \"epochs\": [e[\"Epoch\"] for e in epochs_list],\n", " \"f1\": [e[\"F1\"] for e in epochs_list],\n", " \"loss\": [e[\"Loss\"] for e in epochs_list],\n", " \"precision\": [e[\"Precision\"] for e in epochs_list],\n", " \"recall\": [e[\"Recall\"] for e in epochs_list]\n", " }\n", " return formatted\n", "\n", " metrics_data = {\n", " \"trials\": format_history(search_history),\n", " \"final_run\": {\n", " \"epochs\": [e[\"Epoch\"] for e in final_history],\n", " \"f1\": [e[\"F1\"] for e in final_history],\n", " \"loss\": [e[\"Loss\"] for e in final_history],\n", " \"precision\": [e[\"Precision\"] for e in final_history],\n", " \"recall\": [e[\"Recall\"] for e in final_history]\n", " },\n", " \"best_hp\": {\n", " \"num_frozen_layers\": best_frozen,\n", " \"learning_rate\": best_run.hyperparameters.get(\"learning_rate\"),\n", " \"weight_decay\": best_run.hyperparameters.get(\"weight_decay\"),\n", " \"batch_size\": best_run.hyperparameters.get(\"per_device_train_batch_size\"),\n", " \"warmup_ratio\": best_run.hyperparameters.get(\"warmup_ratio\"),\n", " \"best_f1\": best_run.objective,\n", " }\n", " }\n", "\n", " metrics_file = f\"./gotcha-extractor-model/{save_name}_metrics.json\"\n", " os.makedirs(os.path.dirname(metrics_file), exist_ok=True)\n", " with open(metrics_file, \"w\", encoding=\"utf-8\") as f:\n", " json.dump(metrics_data, f, indent=2)\n", " print(f\"Successfully saved training metrics to {metrics_file}\")\n", " except Exception as e:\n", " print(f\"Error saving training metrics JSON: {e}\")\n", "\n", " if os.path.exists(search_dir):\n", " shutil.rmtree(search_dir)\n", " print(f\"Cleaned up search directory: {search_dir}\")\n", "\n", " print(f\"Finished! Best model saved to {output_dir}\")\n", " return best_run\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "============================================================\n", " OPTUNA SEARCH: google/electra-small-discriminator (10 trials, 10 epochs each)\n", "============================================================\n", " Trainable: 13,483,779 / 13,483,779\n", " [Trial 0] Freezing embeddings + bottom 9/12 layers\n", " Trainable: 2,403,075 / 13,483,779\n", " [Trial 1] Freezing embeddings + bottom 0/12 layers\n", " Trainable: 9,510,915 / 13,483,779\n", " [Trial 2] Freezing embeddings + bottom 3/12 layers\n", " Trainable: 7,141,635 / 13,483,779\n", " [Trial 3] Freezing embeddings + bottom 10/12 layers\n", " Trainable: 1,613,315 / 13,483,779\n", " [Trial 4] Freezing embeddings + bottom 3/12 layers\n", " Trainable: 7,141,635 / 13,483,779\n", " [Trial 5] Freezing embeddings + bottom 10/12 layers\n", " Trainable: 1,613,315 / 13,483,779\n", " [Trial 6] Freezing embeddings + bottom 7/12 layers\n", " Trainable: 3,982,595 / 13,483,779\n", " [Trial 7] Freezing embeddings + bottom 7/12 layers\n", " Trainable: 3,982,595 / 13,483,779\n", " [Trial 8] Freezing embeddings + bottom 11/12 layers\n", " Trainable: 823,555 / 13,483,779\n", " [Trial 9] Freezing embeddings + bottom 1/12 layers\n", " Trainable: 8,721,155 / 13,483,779\n" ] }, { "data": { "text/html": [ "┌─ Trial 1 ─ Best F1: 0.1568 (Epoch 10)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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EpochLossPrecisionRecallF1Accuracy
010.58170.00180.01620.00320.8160
120.40280.02650.18620.04640.8737
230.35950.07950.43720.13450.8909
340.30490.07300.45340.12580.8948
450.27310.07090.54250.12540.8828
560.28860.07810.55470.13690.8772
670.28420.08610.51010.14730.8977
780.29160.09060.53440.15490.9024
890.29290.08520.54660.14750.8939
9100.29760.09130.55470.15680.8987
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" ], "text/plain": [ " Epoch Loss Precision Recall F1 Accuracy\n", "0 1 0.5817 0.0018 0.0162 0.0032 0.8160\n", "1 2 0.4028 0.0265 0.1862 0.0464 0.8737\n", "2 3 0.3595 0.0795 0.4372 0.1345 0.8909\n", "3 4 0.3049 0.0730 0.4534 0.1258 0.8948\n", "4 5 0.2731 0.0709 0.5425 0.1254 0.8828\n", "5 6 0.2886 0.0781 0.5547 0.1369 0.8772\n", "6 7 0.2842 0.0861 0.5101 0.1473 0.8977\n", "7 8 0.2916 0.0906 0.5344 0.1549 0.9024\n", "8 9 0.2929 0.0852 0.5466 0.1475 0.8939\n", "9 10 0.2976 0.0913 0.5547 0.1568 0.8987" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "┌─ Trial 2 ─ Best F1: 0.4330 (Epoch 9)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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EpochLossPrecisionRecallF1Accuracy
010.52040.00880.03640.01410.8663
120.33370.08830.42910.14650.8911
230.32810.17640.56280.26860.9074
340.37190.19950.60730.30030.9120
450.31440.18230.69230.28860.9019
560.37480.21200.71260.32680.9063
670.45020.25480.58700.35540.9140
780.45430.31520.68020.43080.9092
890.47390.32520.64780.43300.9134
9100.42910.29020.70850.41180.9134
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" ], "text/plain": [ " Epoch Loss Precision Recall F1 Accuracy\n", "0 1 0.5204 0.0088 0.0364 0.0141 0.8663\n", "1 2 0.3337 0.0883 0.4291 0.1465 0.8911\n", "2 3 0.3281 0.1764 0.5628 0.2686 0.9074\n", "3 4 0.3719 0.1995 0.6073 0.3003 0.9120\n", "4 5 0.3144 0.1823 0.6923 0.2886 0.9019\n", "5 6 0.3748 0.2120 0.7126 0.3268 0.9063\n", "6 7 0.4502 0.2548 0.5870 0.3554 0.9140\n", "7 8 0.4543 0.3152 0.6802 0.4308 0.9092\n", "8 9 0.4739 0.3252 0.6478 0.4330 0.9134\n", "9 10 0.4291 0.2902 0.7085 0.4118 0.9134" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "┌─ Trial 3 ─ Best F1: 0.2534 (Epoch 9)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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EpochLossPrecisionRecallF1Accuracy
010.64060.00170.00810.00280.8276
120.38880.03260.19030.05570.8820
230.34750.10360.40490.16500.9008
340.34100.11030.46560.17830.9001
450.32430.09510.55470.16230.8753
560.30790.12400.52630.20080.8989
670.32160.15150.51820.23440.9050
780.32990.15300.55060.23940.9010
890.33650.16390.55870.25340.9030
9100.33070.14540.56680.23140.8997
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" ], "text/plain": [ " Epoch Loss Precision Recall F1 Accuracy\n", "0 1 0.6406 0.0017 0.0081 0.0028 0.8276\n", "1 2 0.3888 0.0326 0.1903 0.0557 0.8820\n", "2 3 0.3475 0.1036 0.4049 0.1650 0.9008\n", "3 4 0.3410 0.1103 0.4656 0.1783 0.9001\n", "4 5 0.3243 0.0951 0.5547 0.1623 0.8753\n", "5 6 0.3079 0.1240 0.5263 0.2008 0.8989\n", "6 7 0.3216 0.1515 0.5182 0.2344 0.9050\n", "7 8 0.3299 0.1530 0.5506 0.2394 0.9010\n", "8 9 0.3365 0.1639 0.5587 0.2534 0.9030\n", "9 10 0.3307 0.1454 0.5668 0.2314 0.8997" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "┌─ Trial 4 ─ Best F1: 0.1137 (Epoch 9)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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EpochLossPrecisionRecallF1Accuracy
010.63800.00100.01210.00180.7842
120.51830.00630.05670.01130.8367
230.41980.03360.29150.06030.8613
340.38360.04880.39680.08680.8729
450.34590.05020.45340.09040.8642
560.33810.05670.47770.10140.8646
670.33440.06030.47370.10690.8788
780.33240.05860.46960.10430.8769
890.33720.06450.47770.11370.8815
9100.33460.06280.46960.11080.8810
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EpochLossPrecisionRecallF1Accuracy
010.66310.00080.00400.00130.8209
120.40280.03340.19030.05690.8802
230.37610.09910.36030.15550.8984
340.34830.08970.47770.15110.8896
450.33440.10850.62350.18480.8644
560.33000.12220.54250.19940.8872
670.32360.12750.57490.20870.8896
780.35830.15020.54660.23560.8982
890.35110.14910.57890.23710.8913
9100.35050.14790.56280.23420.8952
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EpochLossPrecisionRecallF1Accuracy
010.78360.00.00.00.7954
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EpochLossPrecisionRecallF1Accuracy
010.69980.00.00.00.805
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EpochLossPrecisionRecallF1Accuracy
010.5470.00540.03240.00920.8343
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EpochLossPrecisionRecallF1Accuracy
010.84950.00.00.00.7988
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EpochLossPrecisionRecallF1Accuracy
010.48070.02690.10120.04250.8781
120.32320.09930.48180.16470.8991
230.30950.15880.57490.24890.9023
340.36060.21360.63560.31980.9173
450.32910.21660.69640.33050.9046
560.39460.25400.70850.37390.9135
670.40610.26430.61540.36980.9157
780.43520.29170.65180.40300.9199
890.45350.30520.65990.41740.9188
9100.43470.28240.69640.40190.9145
\n", "
" ], "text/plain": [ " Epoch Loss Precision Recall F1 Accuracy\n", "0 1 0.4807 0.0269 0.1012 0.0425 0.8781\n", "1 2 0.3232 0.0993 0.4818 0.1647 0.8991\n", "2 3 0.3095 0.1588 0.5749 0.2489 0.9023\n", "3 4 0.3606 0.2136 0.6356 0.3198 0.9173\n", "4 5 0.3291 0.2166 0.6964 0.3305 0.9046\n", "5 6 0.3946 0.2540 0.7085 0.3739 0.9135\n", "6 7 0.4061 0.2643 0.6154 0.3698 0.9157\n", "7 8 0.4352 0.2917 0.6518 0.4030 0.9199\n", "8 9 0.4535 0.3052 0.6599 0.4174 0.9188\n", "9 10 0.4347 0.2824 0.6964 0.4019 0.9145" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "============================================================\n", " BEST HYPERPARAMETERS FOUND\n", "============================================================\n", " num_frozen_layers: 0\n", " learning_rate: 3.25403772619166e-05\n", " weight_decay: 0.13425032573877754\n", " per_device_train_batch_size: 16\n", " warmup_ratio: 0.2246663570650131\n", " Best F1: 0.4118\n", "\n", ">>> Final training with best hyperparameters <<<\n", "Final model — Frozen layers: 0, Trainable: 9,510,915 / 13,483,779\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# [CELL 10 - Model 1: ELECTRA-Small (Optuna)]\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m optuna_train_model(\u001b[33m\"google/electra-small-discriminator\"\u001b[39m, \u001b[33m\"electra-small\"\u001b[39m, n_trials=\u001b[32m10\u001b[39m, num_epochs=\u001b[32m10\u001b[39m)\n", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 182\u001b[39m, in \u001b[36moptuna_train_model\u001b[39m\u001b[34m(model_checkpoint, save_name, n_trials, num_epochs)\u001b[39m\n\u001b[32m 178\u001b[39m callbacks=[metrics_cb],\n\u001b[32m 179\u001b[39m )\n\u001b[32m 180\u001b[39m final_trainer.remove_callback(PrinterCallback)\n\u001b[32m 181\u001b[39m \n\u001b[32m--> \u001b[39m\u001b[32m182\u001b[39m final_trainer.train()\n\u001b[32m 183\u001b[39m metrics_cb.display(label_prefix=\u001b[33m\"Final Training Epoch\"\u001b[39m)\n\u001b[32m 184\u001b[39m \n\u001b[32m 185\u001b[39m \u001b[38;5;66;03m# Capture final training history\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py:1427\u001b[39m, in \u001b[36mTrainer.train\u001b[39m\u001b[34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[39m\n\u001b[32m 1425\u001b[39m ctx = suppress_progress_bars() \u001b[38;5;28;01mif\u001b[39;00m args.push_to_hub \u001b[38;5;28;01melse\u001b[39;00m contextlib.nullcontext()\n\u001b[32m 1426\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m ctx:\n\u001b[32m-> \u001b[39m\u001b[32m1427\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43minner_training_loop\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 1428\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1429\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1430\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1431\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mignore_keys_for_eval\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mignore_keys_for_eval\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1432\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py:1509\u001b[39m, in \u001b[36mTrainer._inner_training_loop\u001b[39m\u001b[34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[39m\n\u001b[32m 1507\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(epochs_trained, num_train_epochs):\n\u001b[32m 1508\u001b[39m \u001b[38;5;28mself\u001b[39m.control = \u001b[38;5;28mself\u001b[39m.callback_handler.on_epoch_begin(\u001b[38;5;28mself\u001b[39m.args, \u001b[38;5;28mself\u001b[39m.state, \u001b[38;5;28mself\u001b[39m.control)\n\u001b[32m-> \u001b[39m\u001b[32m1509\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_run_epoch\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 1510\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1511\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mepoch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mepoch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1512\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtrain_dataloader\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtrain_dataloader\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1513\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43msteps_in_epoch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43msteps_in_epoch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1514\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mnum_update_steps_per_epoch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mnum_update_steps_per_epoch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1515\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1516\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mignore_keys_for_eval\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mignore_keys_for_eval\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1517\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mstart_time\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mstart_time\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1518\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1519\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mepochs_trained\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mepochs_trained\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1520\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43msteps_trained_in_current_epoch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43msteps_trained_in_current_epoch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1521\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1522\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.control.should_training_stop:\n\u001b[32m 1523\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py:1737\u001b[39m, in \u001b[36mTrainer._run_epoch\u001b[39m\u001b[34m(self, model, epoch, train_dataloader, steps_in_epoch, num_update_steps_per_epoch, trial, ignore_keys_for_eval, start_time, resume_from_checkpoint, epochs_trained, steps_trained_in_current_epoch)\u001b[39m\n\u001b[32m 1735\u001b[39m sync_context = functools.partial(\u001b[38;5;28mself\u001b[39m.accelerator.no_sync, model=model)\n\u001b[32m 1736\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m sync_context():\n\u001b[32m-> \u001b[39m\u001b[32m1737\u001b[39m tr_loss_step = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mtraining_step\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43minputs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mnum_items_in_batch\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1739\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m 1740\u001b[39m \u001b[38;5;28mself\u001b[39m.args.logging_nan_inf_filter\n\u001b[32m 1741\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_xla_available()\n\u001b[32m 1742\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m (torch.isnan(tr_loss_step) \u001b[38;5;129;01mor\u001b[39;00m torch.isinf(tr_loss_step))\n\u001b[32m 1743\u001b[39m ):\n\u001b[32m 1744\u001b[39m \u001b[38;5;66;03m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[32m 1745\u001b[39m \u001b[38;5;28mself\u001b[39m._tr_loss += \u001b[38;5;28mself\u001b[39m._tr_loss / (\u001b[32m1\u001b[39m + \u001b[38;5;28mself\u001b[39m.state.global_step - \u001b[38;5;28mself\u001b[39m._globalstep_last_logged)\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py:1937\u001b[39m, in \u001b[36mTrainer.training_step\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 1934\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.accelerator.distributed_type == DistributedType.DEEPSPEED:\n\u001b[32m 1935\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mscale_wrt_gas\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1937\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43maccelerator\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mbackward\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mloss\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1939\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m loss.detach()\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\accelerate\\accelerator.py:2838\u001b[39m, in \u001b[36mAccelerator.backward\u001b[39m\u001b[34m(self, loss, **kwargs)\u001b[39m\n\u001b[32m 2836\u001b[39m \u001b[38;5;28mself\u001b[39m.lomo_backward(loss, learning_rate)\n\u001b[32m 2837\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m2838\u001b[39m \u001b[30;43mloss\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mbackward\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\torch\\_tensor.py:631\u001b[39m, in \u001b[36mTensor.backward\u001b[39m\u001b[34m(self, gradient, retain_graph, create_graph, inputs)\u001b[39m\n\u001b[32m 621\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m 622\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[32m 623\u001b[39m Tensor.backward,\n\u001b[32m 624\u001b[39m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[32m (...)\u001b[39m\u001b[32m 629\u001b[39m inputs=inputs,\n\u001b[32m 630\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m631\u001b[39m \u001b[30;43mtorch\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mautograd\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mbackward\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 632\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mgradient\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mretain_graph\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mcreate_graph\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43minputs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43minputs\u001b[39;49m\n\u001b[32m 633\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\torch\\autograd\\__init__.py:379\u001b[39m, in \u001b[36mbackward\u001b[39m\u001b[34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[39m\n\u001b[32m 374\u001b[39m retain_graph = create_graph\n\u001b[32m 376\u001b[39m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[32m 377\u001b[39m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[32m 378\u001b[39m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m379\u001b[39m \u001b[30;43m_engine_run_backward\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 380\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtensors\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 381\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mgrad_tensors_\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 382\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mretain_graph\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 383\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcreate_graph\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 384\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43minputs_tuple\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 385\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mallow_unreachable\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mTrue\u001b[39;49;00m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 386\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43maccumulate_grad\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mTrue\u001b[39;49;00m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 387\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\torch\\autograd\\graph.py:882\u001b[39m, in \u001b[36m_engine_run_backward\u001b[39m\u001b[34m(t_outputs, *args, **kwargs)\u001b[39m\n\u001b[32m 879\u001b[39m torch._C._stash_obj_in_tls(\u001b[33m\"\u001b[39m\u001b[33mcontext\u001b[39m\u001b[33m\"\u001b[39m, contextvars.copy_context())\n\u001b[32m 881\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m882\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mVariable\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_execution_engine\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mrun_backward\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[32m 883\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mt_outputs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\n\u001b[32m 884\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[32m 885\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 886\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n", "\u001b[31mKeyboardInterrupt\u001b[39m: " ] } ], "source": [ "optuna_train_model(\"google/electra-small-discriminator\", \"electra-small\", n_trials=15, num_epochs=10)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "============================================================\n", " OPTUNA SEARCH: prajjwal1/bert-tiny (10 trials, 10 epochs each)\n", "============================================================\n", " Trainable: 4,369,795 / 4,369,795\n", " [Trial 0] Freezing embeddings + bottom 2/2 layers\n", " Trainable: 387 / 4,369,795\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33m[W 2026-06-03 00:17:04,330]\u001b[0m Trial 0 failed with parameters: {'num_frozen_layers': 2, 'learning_rate': 1.2256259067377313e-05, 'weight_decay': 0.016865461179420643, 'per_device_train_batch_size': 16, 'warmup_ratio': 0.2170090684267037} because of the following error: KeyboardInterrupt().\u001b[0m\n", "Traceback (most recent call last):\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\optuna\\study\\_optimize.py\", line 206, in _run_trial\n", " value_or_values = func(trial)\n", " ^^^^^^^^^^^\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\integrations\\integration_utils.py\", line 257, in _objective\n", " trainer.train(resume_from_checkpoint=checkpoint, trial=trial)\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py\", line 1427, in train\n", " return inner_training_loop(\n", " ^^^^^^^^^^^^^^^^^^^^\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py\", line 1509, in _inner_training_loop\n", " self._run_epoch(\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py\", line 1704, in _run_epoch\n", " batch_samples, num_items_in_batch = self.get_batch_samples(epoch_iterator, num_batches, self.args.device)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py\", line 2105, in get_batch_samples\n", " batch_samples.append(next(epoch_iterator))\n", " ^^^^^^^^^^^^^^^^^^^^\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\accelerate\\data_loader.py\", line 589, in __iter__\n", " next_batch = next(dataloader_iter)\n", " ^^^^^^^^^^^^^^^^^^^^^\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py\", line 718, in __next__\n", " data = self._next_data()\n", " ^^^^^^^^^^^^^^^^^\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py\", line 780, in _next_data\n", " data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\torch\\utils\\data\\_utils\\pin_memory.py\", line 73, in pin_memory\n", " {k: pin_memory(sample, device) for k, sample in data.items()}\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\torch\\utils\\data\\_utils\\pin_memory.py\", line 73, in \n", " {k: pin_memory(sample, device) for k, sample in data.items()}\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\torch\\utils\\data\\_utils\\pin_memory.py\", line 57, in pin_memory\n", " return data.pin_memory()\n", " ^^^^^^^^^^^^^^^^^\n", "KeyboardInterrupt\n", "\u001b[33m[W 2026-06-03 00:17:04,332]\u001b[0m Trial 0 failed with value None.\u001b[0m\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# [CELL 11 - Model 2: BERT-Tiny (Optuna)]\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m optuna_train_model(\u001b[33m\"prajjwal1/bert-tiny\"\u001b[39m, \u001b[33m\"bert-tiny\"\u001b[39m, n_trials=\u001b[32m10\u001b[39m, num_epochs=\u001b[32m10\u001b[39m)\n", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 113\u001b[39m, in \u001b[36moptuna_train_model\u001b[39m\u001b[34m(model_checkpoint, save_name, n_trials, num_epochs)\u001b[39m\n\u001b[32m 109\u001b[39m callbacks=[metrics_cb],\n\u001b[32m 110\u001b[39m )\n\u001b[32m 111\u001b[39m trainer.remove_callback(PrinterCallback)\n\u001b[32m 112\u001b[39m \n\u001b[32m--> \u001b[39m\u001b[32m113\u001b[39m best_run = trainer.hyperparameter_search(\n\u001b[32m 114\u001b[39m direction=\u001b[33m\"maximize\"\u001b[39m,\n\u001b[32m 115\u001b[39m backend=\u001b[33m\"optuna\"\u001b[39m,\n\u001b[32m 116\u001b[39m hp_space=hp_space,\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py:4238\u001b[39m, in \u001b[36mTrainer.hyperparameter_search\u001b[39m\u001b[34m(self, hp_space, compute_objective, n_trials, direction, backend, hp_name, **kwargs)\u001b[39m\n\u001b[32m 4235\u001b[39m \u001b[38;5;28mself\u001b[39m.hp_name = hp_name\n\u001b[32m 4236\u001b[39m \u001b[38;5;28mself\u001b[39m.compute_objective = default_compute_objective \u001b[38;5;28;01mif\u001b[39;00m compute_objective \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m compute_objective\n\u001b[32m-> \u001b[39m\u001b[32m4238\u001b[39m best_run = \u001b[30;43mbackend_obj\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mrun\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mdirection\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 4240\u001b[39m \u001b[38;5;28mself\u001b[39m.hp_search_backend = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 4241\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m best_run\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\hyperparameter_search.py:68\u001b[39m, in \u001b[36mOptunaBackend.run\u001b[39m\u001b[34m(self, trainer, n_trials, direction, **kwargs)\u001b[39m\n\u001b[32m 67\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m, trainer, n_trials: \u001b[38;5;28mint\u001b[39m, direction: \u001b[38;5;28mstr\u001b[39m, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m68\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mrun_hp_search_optuna\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mtrainer\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mdirection\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\integrations\\integration_utils.py:276\u001b[39m, in \u001b[36mrun_hp_search_optuna\u001b[39m\u001b[34m(trainer, n_trials, direction, **kwargs)\u001b[39m\n\u001b[32m 274\u001b[39m direction = \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m directions \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m direction\n\u001b[32m 275\u001b[39m study = optuna.create_study(direction=direction, directions=directions, **kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m276\u001b[39m \u001b[30;43mstudy\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43moptimize\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 277\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m_objective\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mn_jobs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mn_jobs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mgc_after_trial\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mgc_after_trial\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\n\u001b[32m 278\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 279\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m study._is_multi_objective():\n\u001b[32m 280\u001b[39m best_trial = study.best_trial\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\optuna\\study\\study.py:490\u001b[39m, in \u001b[36mStudy.optimize\u001b[39m\u001b[34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[39m\n\u001b[32m 388\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34moptimize\u001b[39m(\n\u001b[32m 389\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 390\u001b[39m func: ObjectiveFuncType,\n\u001b[32m (...)\u001b[39m\u001b[32m 397\u001b[39m show_progress_bar: \u001b[38;5;28mbool\u001b[39m = \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[32m 398\u001b[39m ) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 399\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Optimize an objective function.\u001b[39;00m\n\u001b[32m 400\u001b[39m \n\u001b[32m 401\u001b[39m \u001b[33;03m Optimization is done by choosing a suitable set of hyperparameter values from a given\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 488\u001b[39m \u001b[33;03m If nested invocation of this method occurs.\u001b[39;00m\n\u001b[32m 489\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m490\u001b[39m \u001b[30;43m_optimize\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 491\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mstudy\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 492\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mfunc\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mfunc\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 493\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 494\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 495\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mn_jobs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mn_jobs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 496\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtuple\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43misinstance\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mIterable\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01melse\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 497\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcallbacks\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcallbacks\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 498\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mgc_after_trial\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mgc_after_trial\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 499\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mshow_progress_bar\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mshow_progress_bar\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 500\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\optuna\\study\\_optimize.py:68\u001b[39m, in \u001b[36m_optimize\u001b[39m\u001b[34m(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[39m\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 67\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m n_jobs == \u001b[32m1\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m68\u001b[39m \u001b[30;43m_optimize_sequential\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 69\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mstudy\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 70\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mfunc\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 71\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 72\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 73\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 74\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcallbacks\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 75\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mgc_after_trial\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 76\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mreseed_sampler_rng\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mFalse\u001b[39;49;00m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 77\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtime_start\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 78\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mprogress_bar\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mprogress_bar\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 79\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 80\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 81\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m n_jobs == -\u001b[32m1\u001b[39m:\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\optuna\\study\\_optimize.py:165\u001b[39m, in \u001b[36m_optimize_sequential\u001b[39m\u001b[34m(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)\u001b[39m\n\u001b[32m 162\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 164\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m165\u001b[39m frozen_trial_id = \u001b[30;43m_run_trial\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mstudy\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mfunc\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 166\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 167\u001b[39m \u001b[38;5;66;03m# The following line mitigates memory problems that can be occurred in some\u001b[39;00m\n\u001b[32m 168\u001b[39m \u001b[38;5;66;03m# environments (e.g., services that use computing containers such as GitHub Actions).\u001b[39;00m\n\u001b[32m 169\u001b[39m \u001b[38;5;66;03m# Please refer to the following PR for further details:\u001b[39;00m\n\u001b[32m 170\u001b[39m \u001b[38;5;66;03m# https://github.com/optuna/optuna/pull/325.\u001b[39;00m\n\u001b[32m 171\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m gc_after_trial:\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\optuna\\study\\_optimize.py:263\u001b[39m, in \u001b[36m_run_trial\u001b[39m\u001b[34m(study, func, catch)\u001b[39m\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[33m\"\u001b[39m\u001b[33mShould not reach.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 258\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m 259\u001b[39m updated_state == TrialState.FAIL\n\u001b[32m 260\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m func_err \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 261\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(func_err, catch)\n\u001b[32m 262\u001b[39m ):\n\u001b[32m--> \u001b[39m\u001b[32m263\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m func_err\n\u001b[32m 264\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m trial._trial_id\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\optuna\\study\\_optimize.py:206\u001b[39m, in \u001b[36m_run_trial\u001b[39m\u001b[34m(study, func, catch)\u001b[39m\n\u001b[32m 204\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m get_heartbeat_thread(trial._trial_id, study._storage):\n\u001b[32m 205\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m206\u001b[39m value_or_values = \u001b[30;43mfunc\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 207\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m exceptions.TrialPruned \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 208\u001b[39m \u001b[38;5;66;03m# TODO(mamu): Handle multi-objective cases.\u001b[39;00m\n\u001b[32m 209\u001b[39m state = TrialState.PRUNED\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\integrations\\integration_utils.py:257\u001b[39m, in \u001b[36mrun_hp_search_optuna.._objective\u001b[39m\u001b[34m(trial, checkpoint_dir)\u001b[39m\n\u001b[32m 255\u001b[39m trainer.train(resume_from_checkpoint=checkpoint, trial=trial)\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m257\u001b[39m \u001b[30;43mtrainer\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mtrain\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcheckpoint\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 258\u001b[39m \u001b[38;5;66;03m# If there hasn't been any evaluation during the training loop.\u001b[39;00m\n\u001b[32m 259\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(trainer, \u001b[33m\"\u001b[39m\u001b[33mobjective\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py:1427\u001b[39m, in \u001b[36mTrainer.train\u001b[39m\u001b[34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[39m\n\u001b[32m 1425\u001b[39m ctx = suppress_progress_bars() \u001b[38;5;28;01mif\u001b[39;00m args.push_to_hub \u001b[38;5;28;01melse\u001b[39;00m contextlib.nullcontext()\n\u001b[32m 1426\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m ctx:\n\u001b[32m-> \u001b[39m\u001b[32m1427\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43minner_training_loop\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 1428\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1429\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1430\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1431\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mignore_keys_for_eval\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mignore_keys_for_eval\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1432\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py:1509\u001b[39m, in \u001b[36mTrainer._inner_training_loop\u001b[39m\u001b[34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[39m\n\u001b[32m 1507\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(epochs_trained, num_train_epochs):\n\u001b[32m 1508\u001b[39m \u001b[38;5;28mself\u001b[39m.control = \u001b[38;5;28mself\u001b[39m.callback_handler.on_epoch_begin(\u001b[38;5;28mself\u001b[39m.args, \u001b[38;5;28mself\u001b[39m.state, \u001b[38;5;28mself\u001b[39m.control)\n\u001b[32m-> \u001b[39m\u001b[32m1509\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_run_epoch\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 1510\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1511\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mepoch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mepoch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1512\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtrain_dataloader\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtrain_dataloader\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1513\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43msteps_in_epoch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43msteps_in_epoch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1514\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mnum_update_steps_per_epoch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mnum_update_steps_per_epoch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1515\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1516\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mignore_keys_for_eval\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mignore_keys_for_eval\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1517\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mstart_time\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mstart_time\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1518\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1519\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mepochs_trained\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mepochs_trained\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1520\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43msteps_trained_in_current_epoch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43msteps_trained_in_current_epoch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1521\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1522\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.control.should_training_stop:\n\u001b[32m 1523\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py:1704\u001b[39m, in \u001b[36mTrainer._run_epoch\u001b[39m\u001b[34m(self, model, epoch, train_dataloader, steps_in_epoch, num_update_steps_per_epoch, trial, ignore_keys_for_eval, start_time, resume_from_checkpoint, epochs_trained, steps_trained_in_current_epoch)\u001b[39m\n\u001b[32m 1700\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m update_step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_update_steps_trained, num_update_steps_per_epoch):\n\u001b[32m 1701\u001b[39m num_batches = (\n\u001b[32m 1702\u001b[39m \u001b[38;5;28mself\u001b[39m.args.gradient_accumulation_steps \u001b[38;5;28;01mif\u001b[39;00m update_step != (num_update_steps_per_epoch - \u001b[32m1\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m remainder\n\u001b[32m 1703\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1704\u001b[39m batch_samples, num_items_in_batch = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget_batch_samples\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mepoch_iterator\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mnum_batches\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdevice\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1706\u001b[39m \u001b[38;5;66;03m# This is used to correctly scale the loss when the last accumulation step has fewer batches.\u001b[39;00m\n\u001b[32m 1707\u001b[39m \u001b[38;5;66;03m# Not used if `num_items_in_batch` is not None.\u001b[39;00m\n\u001b[32m 1708\u001b[39m \u001b[38;5;28mself\u001b[39m.current_gradient_accumulation_steps = \u001b[38;5;28mlen\u001b[39m(batch_samples)\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py:2105\u001b[39m, in \u001b[36mTrainer.get_batch_samples\u001b[39m\u001b[34m(self, epoch_iterator, num_batches, device)\u001b[39m\n\u001b[32m 2103\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_batches):\n\u001b[32m 2104\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m2105\u001b[39m batch_samples.append(\u001b[38;5;28mnext\u001b[39m(epoch_iterator))\n\u001b[32m 2106\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[32m 2107\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\accelerate\\data_loader.py:589\u001b[39m, in \u001b[36mDataLoaderShard.__iter__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 587\u001b[39m current_batch = send_to_device(current_batch, \u001b[38;5;28mself\u001b[39m.device, non_blocking=\u001b[38;5;28mself\u001b[39m._non_blocking)\n\u001b[32m 588\u001b[39m \u001b[38;5;28mself\u001b[39m._update_state_dict()\n\u001b[32m--> \u001b[39m\u001b[32m589\u001b[39m next_batch = \u001b[38;5;28mnext\u001b[39m(dataloader_iter)\n\u001b[32m 590\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m batch_index >= \u001b[38;5;28mself\u001b[39m.skip_batches:\n\u001b[32m 591\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m current_batch\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:718\u001b[39m, in \u001b[36m_BaseDataLoaderIter.__next__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 715\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 716\u001b[39m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[32m 717\u001b[39m \u001b[38;5;28mself\u001b[39m._reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m718\u001b[39m data = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_next_data\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 719\u001b[39m \u001b[38;5;28mself\u001b[39m._num_yielded += \u001b[32m1\u001b[39m\n\u001b[32m 720\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m 721\u001b[39m \u001b[38;5;28mself\u001b[39m._dataset_kind == _DatasetKind.Iterable\n\u001b[32m 722\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m._IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 723\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m._num_yielded > \u001b[38;5;28mself\u001b[39m._IterableDataset_len_called\n\u001b[32m 724\u001b[39m ):\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:780\u001b[39m, in \u001b[36m_SingleProcessDataLoaderIter._next_data\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 778\u001b[39m data = \u001b[38;5;28mself\u001b[39m._dataset_fetcher.fetch(index) \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[32m 779\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._pin_memory:\n\u001b[32m--> \u001b[39m\u001b[32m780\u001b[39m data = \u001b[30;43m_utils\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mpin_memory\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mpin_memory\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mdata\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_pin_memory_device\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 781\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m data\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\torch\\utils\\data\\_utils\\pin_memory.py:73\u001b[39m, in \u001b[36mpin_memory\u001b[39m\u001b[34m(data, device)\u001b[39m\n\u001b[32m 67\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, collections.abc.MutableMapping):\n\u001b[32m 68\u001b[39m \u001b[38;5;66;03m# The mapping type may have extra properties, so we can't just\u001b[39;00m\n\u001b[32m 69\u001b[39m \u001b[38;5;66;03m# use `type(data)(...)` to create the new mapping.\u001b[39;00m\n\u001b[32m 70\u001b[39m \u001b[38;5;66;03m# Create a clone and update it if the mapping type is mutable.\u001b[39;00m\n\u001b[32m 71\u001b[39m clone = copy.copy(data)\n\u001b[32m 72\u001b[39m clone.update(\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m \u001b[30;43m{\u001b[39;49m\u001b[30;43mk\u001b[39;49m\u001b[30;43m:\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mpin_memory\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43msample\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mdevice\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mfor\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mk\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43msample\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01min\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mdata\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mitems\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m}\u001b[39;49m\n\u001b[32m 74\u001b[39m )\n\u001b[32m 75\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m clone\n\u001b[32m 76\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 77\u001b[39m \u001b[38;5;66;03m# pyrefly: ignore [bad-instantiation]\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\torch\\utils\\data\\_utils\\pin_memory.py:73\u001b[39m, in \u001b[36m\u001b[39m\u001b[34m(.0)\u001b[39m\n\u001b[32m 67\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, collections.abc.MutableMapping):\n\u001b[32m 68\u001b[39m \u001b[38;5;66;03m# The mapping type may have extra properties, so we can't just\u001b[39;00m\n\u001b[32m 69\u001b[39m \u001b[38;5;66;03m# use `type(data)(...)` to create the new mapping.\u001b[39;00m\n\u001b[32m 70\u001b[39m \u001b[38;5;66;03m# Create a clone and update it if the mapping type is mutable.\u001b[39;00m\n\u001b[32m 71\u001b[39m clone = copy.copy(data)\n\u001b[32m 72\u001b[39m clone.update(\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m {k: \u001b[30;43mpin_memory\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43msample\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mdevice\u001b[39;49m\u001b[30;43m)\u001b[39;49m \u001b[38;5;28;01mfor\u001b[39;00m k, sample \u001b[38;5;129;01min\u001b[39;00m data.items()}\n\u001b[32m 74\u001b[39m )\n\u001b[32m 75\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m clone\n\u001b[32m 76\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 77\u001b[39m \u001b[38;5;66;03m# pyrefly: ignore [bad-instantiation]\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\torch\\utils\\data\\_utils\\pin_memory.py:57\u001b[39m, in \u001b[36mpin_memory\u001b[39m\u001b[34m(data, device)\u001b[39m\n\u001b[32m 55\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpin_memory\u001b[39m(data, device=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 56\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, torch.Tensor):\n\u001b[32m---> \u001b[39m\u001b[32m57\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mdata\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mpin_memory\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 59\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(data, \u001b[33m\"\u001b[39m\u001b[33mpin_memory\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m 60\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m data.pin_memory()\n", "\u001b[31mKeyboardInterrupt\u001b[39m: " ] } ], "source": [ "optuna_train_model(\"prajjwal1/bert-tiny\", \"bert-tiny\", n_trials=15, num_epochs=10)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "============================================================\n", " OPTUNA SEARCH: huawei-noah/TinyBERT_General_4L_312D (10 trials, 10 epochs each)\n", "============================================================\n", " Trainable: 14,253,531 / 14,253,531\n", " [Trial 0] Freezing embeddings + bottom 1/4 layers\n", " Trainable: 3,427,491 / 14,253,531\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33m[W 2026-05-30 11:48:34,107]\u001b[0m Trial 0 failed with parameters: {'num_frozen_layers': 1, 'learning_rate': 1.3463949089094e-05, 'weight_decay': 0.12284462881893396, 'per_device_train_batch_size': 16, 'warmup_ratio': 0.1806284168048427} because of the following error: KeyboardInterrupt().\u001b[0m\n", "Traceback (most recent call last):\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\optuna\\study\\_optimize.py\", line 206, in _run_trial\n", " value_or_values = func(trial)\n", " ^^^^^^^^^^^\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\integrations\\integration_utils.py\", line 257, in _objective\n", " trainer.train(resume_from_checkpoint=checkpoint, trial=trial)\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py\", line 1427, in train\n", " return inner_training_loop(\n", " ^^^^^^^^^^^^^^^^^^^^\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py\", line 1509, in _inner_training_loop\n", " self._run_epoch(\n", " File \"d:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py\", line 1742, in _run_epoch\n", " and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step))\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", "KeyboardInterrupt\n", "\u001b[33m[W 2026-05-30 11:48:34,108]\u001b[0m Trial 0 failed with value None.\u001b[0m\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# [CELL 9 - Model 3: TinyBERT General 4L 312D (Optuna)]\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m optuna_train_model(\u001b[33m\"huawei-noah/TinyBERT_General_4L_312D\"\u001b[39m, \u001b[33m\"tinybert\"\u001b[39m, n_trials=\u001b[32m10\u001b[39m, num_epochs=\u001b[32m10\u001b[39m)\n", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 403\u001b[39m, in \u001b[36moptuna_train_model\u001b[39m\u001b[34m(model_checkpoint, save_name, n_trials, num_epochs)\u001b[39m\n\u001b[32m 399\u001b[39m )\n\u001b[32m 400\u001b[39m trainer.remove_callback(PrinterCallback)\n\u001b[32m 401\u001b[39m \n\u001b[32m 402\u001b[39m \u001b[38;5;66;03m# 8. Run the search\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m403\u001b[39m best_run = trainer.hyperparameter_search(\n\u001b[32m 404\u001b[39m direction=\u001b[33m\"maximize\"\u001b[39m,\n\u001b[32m 405\u001b[39m backend=\u001b[33m\"optuna\"\u001b[39m,\n\u001b[32m 406\u001b[39m hp_space=hp_space,\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py:4238\u001b[39m, in \u001b[36mTrainer.hyperparameter_search\u001b[39m\u001b[34m(self, hp_space, compute_objective, n_trials, direction, backend, hp_name, **kwargs)\u001b[39m\n\u001b[32m 4235\u001b[39m \u001b[38;5;28mself\u001b[39m.hp_name = hp_name\n\u001b[32m 4236\u001b[39m \u001b[38;5;28mself\u001b[39m.compute_objective = default_compute_objective \u001b[38;5;28;01mif\u001b[39;00m compute_objective \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m compute_objective\n\u001b[32m-> \u001b[39m\u001b[32m4238\u001b[39m best_run = \u001b[30;43mbackend_obj\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mrun\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mdirection\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 4240\u001b[39m \u001b[38;5;28mself\u001b[39m.hp_search_backend = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 4241\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m best_run\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\hyperparameter_search.py:68\u001b[39m, in \u001b[36mOptunaBackend.run\u001b[39m\u001b[34m(self, trainer, n_trials, direction, **kwargs)\u001b[39m\n\u001b[32m 67\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m, trainer, n_trials: \u001b[38;5;28mint\u001b[39m, direction: \u001b[38;5;28mstr\u001b[39m, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m68\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mrun_hp_search_optuna\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mtrainer\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mdirection\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\integrations\\integration_utils.py:276\u001b[39m, in \u001b[36mrun_hp_search_optuna\u001b[39m\u001b[34m(trainer, n_trials, direction, **kwargs)\u001b[39m\n\u001b[32m 274\u001b[39m direction = \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m directions \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m direction\n\u001b[32m 275\u001b[39m study = optuna.create_study(direction=direction, directions=directions, **kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m276\u001b[39m \u001b[30;43mstudy\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43moptimize\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 277\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m_objective\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mn_jobs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mn_jobs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mgc_after_trial\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mgc_after_trial\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\n\u001b[32m 278\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 279\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m study._is_multi_objective():\n\u001b[32m 280\u001b[39m best_trial = study.best_trial\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\optuna\\study\\study.py:490\u001b[39m, in \u001b[36mStudy.optimize\u001b[39m\u001b[34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[39m\n\u001b[32m 388\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34moptimize\u001b[39m(\n\u001b[32m 389\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 390\u001b[39m func: ObjectiveFuncType,\n\u001b[32m (...)\u001b[39m\u001b[32m 397\u001b[39m show_progress_bar: \u001b[38;5;28mbool\u001b[39m = \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[32m 398\u001b[39m ) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 399\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Optimize an objective function.\u001b[39;00m\n\u001b[32m 400\u001b[39m \n\u001b[32m 401\u001b[39m \u001b[33;03m Optimization is done by choosing a suitable set of hyperparameter values from a given\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 488\u001b[39m \u001b[33;03m If nested invocation of this method occurs.\u001b[39;00m\n\u001b[32m 489\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m490\u001b[39m \u001b[30;43m_optimize\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 491\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mstudy\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 492\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mfunc\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mfunc\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 493\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 494\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 495\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mn_jobs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mn_jobs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 496\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtuple\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43misinstance\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mIterable\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01melse\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 497\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcallbacks\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcallbacks\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 498\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mgc_after_trial\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mgc_after_trial\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 499\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mshow_progress_bar\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mshow_progress_bar\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 500\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\optuna\\study\\_optimize.py:68\u001b[39m, in \u001b[36m_optimize\u001b[39m\u001b[34m(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[39m\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 67\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m n_jobs == \u001b[32m1\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m68\u001b[39m \u001b[30;43m_optimize_sequential\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 69\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mstudy\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 70\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mfunc\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 71\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mn_trials\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 72\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 73\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 74\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcallbacks\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 75\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mgc_after_trial\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 76\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mreseed_sampler_rng\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mFalse\u001b[39;49;00m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 77\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtime_start\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 78\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mprogress_bar\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mprogress_bar\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 79\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 80\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 81\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m n_jobs == -\u001b[32m1\u001b[39m:\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\optuna\\study\\_optimize.py:165\u001b[39m, in \u001b[36m_optimize_sequential\u001b[39m\u001b[34m(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)\u001b[39m\n\u001b[32m 162\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 164\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m165\u001b[39m frozen_trial_id = \u001b[30;43m_run_trial\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mstudy\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mfunc\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mcatch\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 166\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 167\u001b[39m \u001b[38;5;66;03m# The following line mitigates memory problems that can be occurred in some\u001b[39;00m\n\u001b[32m 168\u001b[39m \u001b[38;5;66;03m# environments (e.g., services that use computing containers such as GitHub Actions).\u001b[39;00m\n\u001b[32m 169\u001b[39m \u001b[38;5;66;03m# Please refer to the following PR for further details:\u001b[39;00m\n\u001b[32m 170\u001b[39m \u001b[38;5;66;03m# https://github.com/optuna/optuna/pull/325.\u001b[39;00m\n\u001b[32m 171\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m gc_after_trial:\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\optuna\\study\\_optimize.py:263\u001b[39m, in \u001b[36m_run_trial\u001b[39m\u001b[34m(study, func, catch)\u001b[39m\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[33m\"\u001b[39m\u001b[33mShould not reach.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 258\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m 259\u001b[39m updated_state == TrialState.FAIL\n\u001b[32m 260\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m func_err \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 261\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(func_err, catch)\n\u001b[32m 262\u001b[39m ):\n\u001b[32m--> \u001b[39m\u001b[32m263\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m func_err\n\u001b[32m 264\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m trial._trial_id\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\optuna\\study\\_optimize.py:206\u001b[39m, in \u001b[36m_run_trial\u001b[39m\u001b[34m(study, func, catch)\u001b[39m\n\u001b[32m 204\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m get_heartbeat_thread(trial._trial_id, study._storage):\n\u001b[32m 205\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m206\u001b[39m value_or_values = \u001b[30;43mfunc\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 207\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m exceptions.TrialPruned \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 208\u001b[39m \u001b[38;5;66;03m# TODO(mamu): Handle multi-objective cases.\u001b[39;00m\n\u001b[32m 209\u001b[39m state = TrialState.PRUNED\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\integrations\\integration_utils.py:257\u001b[39m, in \u001b[36mrun_hp_search_optuna.._objective\u001b[39m\u001b[34m(trial, checkpoint_dir)\u001b[39m\n\u001b[32m 255\u001b[39m trainer.train(resume_from_checkpoint=checkpoint, trial=trial)\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m257\u001b[39m \u001b[30;43mtrainer\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mtrain\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcheckpoint\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 258\u001b[39m \u001b[38;5;66;03m# If there hasn't been any evaluation during the training loop.\u001b[39;00m\n\u001b[32m 259\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(trainer, \u001b[33m\"\u001b[39m\u001b[33mobjective\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py:1427\u001b[39m, in \u001b[36mTrainer.train\u001b[39m\u001b[34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[39m\n\u001b[32m 1425\u001b[39m ctx = suppress_progress_bars() \u001b[38;5;28;01mif\u001b[39;00m args.push_to_hub \u001b[38;5;28;01melse\u001b[39;00m contextlib.nullcontext()\n\u001b[32m 1426\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m ctx:\n\u001b[32m-> \u001b[39m\u001b[32m1427\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43minner_training_loop\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 1428\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1429\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1430\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1431\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mignore_keys_for_eval\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mignore_keys_for_eval\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1432\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py:1509\u001b[39m, in \u001b[36mTrainer._inner_training_loop\u001b[39m\u001b[34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[39m\n\u001b[32m 1507\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(epochs_trained, num_train_epochs):\n\u001b[32m 1508\u001b[39m \u001b[38;5;28mself\u001b[39m.control = \u001b[38;5;28mself\u001b[39m.callback_handler.on_epoch_begin(\u001b[38;5;28mself\u001b[39m.args, \u001b[38;5;28mself\u001b[39m.state, \u001b[38;5;28mself\u001b[39m.control)\n\u001b[32m-> \u001b[39m\u001b[32m1509\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_run_epoch\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 1510\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1511\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mepoch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mepoch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1512\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtrain_dataloader\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtrain_dataloader\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1513\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43msteps_in_epoch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43msteps_in_epoch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1514\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mnum_update_steps_per_epoch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mnum_update_steps_per_epoch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1515\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtrial\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1516\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mignore_keys_for_eval\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mignore_keys_for_eval\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1517\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mstart_time\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mstart_time\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1518\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mresume_from_checkpoint\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1519\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mepochs_trained\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mepochs_trained\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1520\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43msteps_trained_in_current_epoch\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43msteps_trained_in_current_epoch\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1521\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1522\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.control.should_training_stop:\n\u001b[32m 1523\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\VSC\\nlp_aol\\.venv\\Lib\\site-packages\\transformers\\trainer.py:1742\u001b[39m, in \u001b[36mTrainer._run_epoch\u001b[39m\u001b[34m(self, model, epoch, train_dataloader, steps_in_epoch, num_update_steps_per_epoch, trial, ignore_keys_for_eval, start_time, resume_from_checkpoint, epochs_trained, steps_trained_in_current_epoch)\u001b[39m\n\u001b[32m 1736\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m sync_context():\n\u001b[32m 1737\u001b[39m tr_loss_step = \u001b[38;5;28mself\u001b[39m.training_step(model, inputs, num_items_in_batch)\n\u001b[32m 1739\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m 1740\u001b[39m \u001b[38;5;28mself\u001b[39m.args.logging_nan_inf_filter\n\u001b[32m 1741\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_xla_available()\n\u001b[32m-> \u001b[39m\u001b[32m1742\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m (torch.isnan(tr_loss_step) \u001b[38;5;129;01mor\u001b[39;00m \u001b[30;43mtorch\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43misinf\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mtr_loss_step\u001b[39;49m\u001b[30;43m)\u001b[39;49m)\n\u001b[32m 1743\u001b[39m ):\n\u001b[32m 1744\u001b[39m \u001b[38;5;66;03m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[32m 1745\u001b[39m \u001b[38;5;28mself\u001b[39m._tr_loss += \u001b[38;5;28mself\u001b[39m._tr_loss / (\u001b[32m1\u001b[39m + \u001b[38;5;28mself\u001b[39m.state.global_step - \u001b[38;5;28mself\u001b[39m._globalstep_last_logged)\n\u001b[32m 1746\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", "\u001b[31mKeyboardInterrupt\u001b[39m: " ] } ], "source": [ "optuna_train_model(\"huawei-noah/TinyBERT_General_4L_312D\", \"tinybert\", n_trials=15, num_epochs=10)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "optuna_train_model(\"prajjwal1/bert-mini\", \"bert-mini\", n_trials=15, num_epochs=10)\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading live training metrics from ./gotcha-extractor-model/electra-small_metrics.json for electra-small...\n" ] }, { "data": { "image/png": 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", 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", 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", 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Best Hyperparameters Summary for electra-small:\n", " num_frozen_layers: 0\n", " learning_rate: 3.25515750502575e-05\n", " weight_decay: 0.10843350203717517\n", " batch_size: 8\n", " warmup_ratio: 0.11726619706630656\n", " best_f1: 0.473202614379085\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.ticker as mticker\n", "import numpy as np\n", "import os\n", "import json\n", "\n", "model_to_visualize = \"electra-small\"\n", "\n", "plt.rcParams.update({\n", " 'figure.facecolor': '#1a1a2e',\n", " 'axes.facecolor': '#16213e',\n", " 'axes.edgecolor': '#e94560',\n", " 'axes.labelcolor': '#eee',\n", " 'text.color': '#eee',\n", " 'xtick.color': '#aaa',\n", " 'ytick.color': '#aaa',\n", " 'grid.color': '#333',\n", " 'grid.alpha': 0.4,\n", " 'font.family': 'sans-serif',\n", " 'font.size': 10,\n", "})\n", "\n", "metrics_file = f\"./gotcha-extractor-model/{model_to_visualize}_metrics.json\"\n", "\n", "print(f\"Loading live training metrics from {metrics_file} for {model_to_visualize}...\")\n", "with open(metrics_file, \"r\", encoding=\"utf-8\") as f:\n", " data = json.load(f)\n", "trials = {int(k): v for k, v in data[\"trials\"].items()}\n", "final_run = data[\"final_run\"]\n", "best_hp = data[\"best_hp\"]\n", "\n", "fig, ax = plt.subplots(figsize=(14, 6))\n", "cmap = plt.cm.viridis(np.linspace(0.15, 0.95, len(trials)))\n", "\n", "for t_num in sorted(trials.keys()):\n", " t = trials[t_num]\n", " if t['f1'] and max(t['f1']) > 0.01:\n", " color_idx = (t_num - 1) % len(cmap)\n", " ax.plot(t['epochs'], t['f1'], marker='o', markersize=4, linewidth=1.8,\n", " color=cmap[color_idx], alpha=0.75, label=f'Trial {t_num} (peak {max(t[\"f1\"]):.4f})')\n", "\n", "ax.set_xlabel('Epoch', fontsize=12)\n", "ax.set_ylabel('Validation F1 Score', fontsize=12)\n", "ax.set_title(f'{model_to_visualize.upper()} Optuna Search: F1 Progression per Trial', fontsize=14, fontweight='bold')\n", "ax.legend(fontsize=8, loc='lower right', framealpha=0.6)\n", "ax.grid(True, linestyle='--')\n", "ax.set_xlim(0.5, max([max(t['epochs']) for t in trials.values() if t['epochs']] + [10.5]))\n", "ax.xaxis.set_major_locator(mticker.MultipleLocator(1))\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "valid_trials = {k: v for k, v in trials.items() if v['f1']}\n", "if valid_trials:\n", " best_trial_num = max(valid_trials.keys(), key=lambda k: max(valid_trials[k]['f1']))\n", " best_trial = trials[best_trial_num]\n", " best_f1_val = max(best_trial['f1'])\n", "else:\n", " best_trial_num = 1\n", " best_trial = trials[1]\n", " best_f1_val = 0.0\n", "\n", "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", "fig.suptitle(f'Best Optuna Trial (Trial {best_trial_num}) — Detailed Metrics',\n", " fontsize=14, fontweight='bold', y=1.01)\n", "\n", "metrics_config = [\n", " ('f1', 'F1 Score', '#00d2ff', axes[0, 0]),\n", " ('loss', 'Validation Loss', '#e94560', axes[0, 1]),\n", " ('precision', 'Precision', '#0f3460', axes[1, 0]),\n", " ('recall', 'Recall', '#a855f7', axes[1, 1]),\n", "]\n", "\n", "for key, title, color, ax in metrics_config:\n", " vals = best_trial[key]\n", " if vals:\n", " ax.plot(best_trial['epochs'], vals, marker='s', markersize=6, linewidth=2.2,\n", " color=color, zorder=5)\n", " ax.fill_between(best_trial['epochs'], vals, alpha=0.15, color=color)\n", " if key == 'loss':\n", " best_idx = np.argmin(vals)\n", " label = f'Min: {vals[best_idx]:.4f}'\n", " else:\n", " best_idx = np.argmax(vals)\n", " label = f'Peak: {vals[best_idx]:.4f}'\n", " ax.annotate(label, xy=(best_trial['epochs'][best_idx], vals[best_idx]),\n", " xytext=(10, 10), textcoords='offset points',\n", " fontsize=9, color=color, fontweight='bold',\n", " arrowprops=dict(arrowstyle='->', color=color, lw=1.2))\n", " ax.set_title(title, fontsize=12)\n", " ax.set_xlabel('Epoch')\n", " ax.grid(True, linestyle='--')\n", " if best_trial['epochs']:\n", " ax.xaxis.set_major_locator(mticker.MultipleLocator(1))\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", "fig.suptitle(f'{model_to_visualize.upper()} Final Training (Best Hyperparameters) — Metrics',\n", " fontsize=14, fontweight='bold', y=1.01)\n", "\n", "metrics_config = [\n", " ('f1', 'F1 Score', '#00d2ff', axes[0, 0]),\n", " ('loss', 'Validation Loss', '#e94560', axes[0, 1]),\n", " ('precision', 'Precision', '#0f3460', axes[1, 0]),\n", " ('recall', 'Recall', '#a855f7', axes[1, 1]),\n", "]\n", "\n", "for key, title, color, ax in metrics_config:\n", " vals = final_run[key]\n", " if vals:\n", " ax.plot(final_run['epochs'], vals, marker='s', markersize=6, linewidth=2.2,\n", " color=color, zorder=5)\n", " ax.fill_between(final_run['epochs'], vals, alpha=0.15, color=color)\n", " if key == 'loss':\n", " best_idx = np.argmin(vals)\n", " label = f'Min: {vals[best_idx]:.4f}'\n", " else:\n", " best_idx = np.argmax(vals)\n", " label = f'Peak: {vals[best_idx]:.4f}'\n", " ax.annotate(label, xy=(final_run['epochs'][best_idx], vals[best_idx]),\n", " xytext=(10, 10), textcoords='offset points',\n", " fontsize=9, color=color, fontweight='bold',\n", " arrowprops=dict(arrowstyle='->', color=color, lw=1.2))\n", " ax.set_title(title, fontsize=12)\n", " ax.set_xlabel('Epoch')\n", " ax.grid(True, linestyle='--')\n", " if final_run['epochs']:\n", " ax.xaxis.set_major_locator(mticker.MultipleLocator(1))\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "fig, ax = plt.subplots(figsize=(12, 5))\n", "trial_nums = list(sorted(trials.keys()))\n", "peak_f1s = [max(trials[t]['f1']) if trials[t]['f1'] else 0.0 for t in trial_nums]\n", "\n", "if peak_f1s:\n", " colors = ['#00d2ff' if f == max(peak_f1s) else '#0f3460' for f in peak_f1s]\n", " bars = ax.bar(trial_nums, peak_f1s, color=colors, edgecolor='#e94560', linewidth=0.8)\n", "\n", " for bar, f1 in zip(bars, peak_f1s):\n", " if f1 > 0.01:\n", " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", " f'{f1:.4f}', ha='center', va='bottom', fontsize=9, fontweight='bold')\n", "\n", "ax.set_xlabel('Trial Number', fontsize=12)\n", "ax.set_ylabel('Peak Validation F1', fontsize=12)\n", "ax.set_title(f'{model_to_visualize.upper()} Optuna Search: Peak F1 per Trial', fontsize=14, fontweight='bold')\n", "ax.set_xticks(trial_nums)\n", "ax.grid(True, axis='y', linestyle='--')\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(f\"\\nBest Hyperparameters Summary for {model_to_visualize}:\")\n", "for k, v in best_hp.items():\n", " print(f\" {k}: {v}\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.colors as mcolors\n", "import numpy as np\n", "from sklearn.metrics import confusion_matrix\n", "from transformers import AutoTokenizer, AutoModelForTokenClassification, BertForTokenClassification\n", "import torch\n", "\n", "model_to_visualize = \"electra-small\"\n", "\n", "plt.rcParams.update({\n", " 'figure.facecolor': '#1a1a2e',\n", " 'axes.facecolor': '#16213e',\n", " 'axes.edgecolor': '#e94560',\n", " 'axes.labelcolor': '#eee',\n", " 'text.color': '#eee',\n", " 'xtick.color': '#aaa',\n", " 'ytick.color': '#aaa',\n", " 'grid.color': '#333',\n", " 'grid.alpha': 0.4,\n", " 'font.family': 'sans-serif',\n", " 'font.size': 11,\n", "})\n", "\n", "model_dir = f\"./gotcha-extractor-model/{model_to_visualize}\"\n", "print(f\"Loading model from {model_dir}...\")\n", "\n", "cm_tokenizer = AutoTokenizer.from_pretrained(model_dir)\n", "\n", "model_cls_key = [k for k in MODEL_CLASS_MAP if model_to_visualize in k]\n", "if model_cls_key:\n", " cm_model = MODEL_CLASS_MAP[model_cls_key[0]].from_pretrained(model_dir).to(device)\n", "else:\n", " cm_model = AutoModelForTokenClassification.from_pretrained(model_dir).to(device)\n", "cm_model.eval()\n", "\n", "print(f\"Running predictions on {len(dataset_splits['test'])} test samples...\")\n", "\n", "y_true = []\n", "y_pred = []\n", "\n", "for example in dataset_splits[\"test\"]:\n", " has_gotcha = 1 if example[\"gotchas\"] else 0\n", " y_true.append(has_gotcha)\n", "\n", " tokenized = tokenize_and_align_labels(example, cm_tokenizer)\n", " input_ids = torch.tensor([tokenized[\"input_ids\"]], device=device)\n", " attention_mask = torch.tensor([tokenized[\"attention_mask\"]], device=device)\n", "\n", " with torch.no_grad():\n", " outputs = cm_model(input_ids=input_ids, attention_mask=attention_mask)\n", " preds = torch.argmax(outputs.logits, dim=2).squeeze(0).cpu().tolist()\n", "\n", " pred_gotcha = 0\n", " for pred_label, true_label in zip(preds, tokenized[\"labels\"]):\n", " if true_label != -100 and pred_label in (1, 2):\n", " pred_gotcha = 1\n", " break\n", " y_pred.append(pred_gotcha)\n", "\n", "n_true_gotcha = sum(y_true)\n", "n_pred_gotcha = sum(y_pred)\n", "print(f\"Samples with gotchas (true): {n_true_gotcha}/{len(y_true)}\")\n", "print(f\"Samples with gotchas (pred): {n_pred_gotcha}/{len(y_pred)}\")\n", "\n", "label_names = [\"No Gotcha\", \"Gotcha\"]\n", "cm = confusion_matrix(y_true, y_pred, labels=[0, 1])\n", "\n", "cm_normalized = cm.astype(\"float\") / cm.sum(axis=1, keepdims=True)\n", "cm_normalized = np.nan_to_num(cm_normalized)\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "fig.suptitle(\n", " f\"{model_to_visualize.upper()} — Sample-Level Confusion Matrix (Gotcha Detection)\",\n", " fontsize=15, fontweight=\"bold\", y=1.02\n", ")\n", "\n", "ax = axes[0]\n", "cmap_raw = mcolors.LinearSegmentedColormap.from_list(\"custom_blues\", [\"#16213e\", \"#00d2ff\", \"#e94560\"])\n", "im = ax.imshow(cm, interpolation=\"nearest\", cmap=cmap_raw, aspect=\"auto\")\n", "\n", "ax.set_xticks(range(len(label_names)))\n", "ax.set_yticks(range(len(label_names)))\n", "ax.set_xticklabels(label_names, fontsize=13, fontweight=\"bold\")\n", "ax.set_yticklabels(label_names, fontsize=13, fontweight=\"bold\")\n", "ax.set_xlabel(\"Predicted\", fontsize=13)\n", "ax.set_ylabel(\"Actual\", fontsize=13)\n", "ax.set_title(\"Raw Counts\", fontsize=13, fontweight=\"bold\", pad=12)\n", "\n", "thresh = cm.max() / 2.0\n", "for i in range(len(label_names)):\n", " for j in range(len(label_names)):\n", " ax.text(j, i, f\"{cm[i, j]:,}\",\n", " ha=\"center\", va=\"center\", fontsize=18, fontweight=\"bold\",\n", " color=\"white\" if cm[i, j] > thresh else \"#00d2ff\")\n", "\n", "ax = axes[1]\n", "cmap_pct = mcolors.LinearSegmentedColormap.from_list(\"custom_purples\", [\"#16213e\", \"#a855f7\", \"#e94560\"])\n", "im2 = ax.imshow(cm_normalized, interpolation=\"nearest\", cmap=cmap_pct, aspect=\"auto\", vmin=0, vmax=1)\n", "\n", "ax.set_xticks(range(len(label_names)))\n", "ax.set_yticks(range(len(label_names)))\n", "ax.set_xticklabels(label_names, fontsize=13, fontweight=\"bold\")\n", "ax.set_yticklabels(label_names, fontsize=13, fontweight=\"bold\")\n", "ax.set_xlabel(\"Predicted\", fontsize=13)\n", "ax.set_ylabel(\"Actual\", fontsize=13)\n", "ax.set_title(\"Normalized (%)\", fontsize=13, fontweight=\"bold\", pad=12)\n", "\n", "for i in range(len(label_names)):\n", " for j in range(len(label_names)):\n", " pct = cm_normalized[i, j] * 100\n", " ax.text(j, i, f\"{pct:.1f}%\",\n", " ha=\"center\", va=\"center\", fontsize=18, fontweight=\"bold\",\n", " color=\"white\" if cm_normalized[i, j] > 0.5 else \"#a855f7\")\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "tn, fp, fn, tp = cm.ravel()\n", "precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n", "recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n", "f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0\n", "accuracy = (tp + tn) / (tp + tn + fp + fn)\n", "\n", "print('\\n' + '='*60)\n", "print(f' GOTCHA DETECTION SUMMARY — {model_to_visualize.upper()}')\n", "print('='*60)\n", "print(f\" Total samples: {len(y_true)}\")\n", "print(f\" True Positives: {tp} (correctly flagged gotchas)\")\n", "print(f\" True Negatives: {tn} (correctly passed clean samples)\")\n", "print(f\" False Positives: {fp} (false alarms)\")\n", "print(f\" False Negatives: {fn} (missed gotchas)\")\n", "print(f\" ─────────────────────────────────────\")\n", "print(f\" Accuracy: {accuracy:.4f}\")\n", "print(f\" Precision: {precision:.4f}\")\n", "print(f\" Recall: {recall:.4f}\")\n", "print(f\" F1 Score: {f1:.4f}\")\n", "print()\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 2 }