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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "b00e4cd9",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"!hf download SaladTechnologies/fiction-ner-750m --quiet --repo-type=dataset --local-dir .\n",
"!unzip -q data.zip"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b1be4895",
"metadata": {},
"outputs": [],
"source": [
"import string\n",
"import random\n",
"\n",
"def get_random_string(length=8):\n",
" \"\"\"Generate a random string of fixed length.\"\"\"\n",
" letters = string.ascii_letters\n",
" return ''.join(random.choice(letters) for i in range(length))\n",
"\n",
"run_name = f\"ner-{get_random_string(8)}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f21e8995",
"metadata": {},
"outputs": [],
"source": [
"from accelerate import notebook_launcher\n",
"import os\n",
"\n",
"\n",
"cuda_visible_devices = os.getenv(\"CUDA_VISIBLE_DEVICES\", \"0\")\n",
"num_devices = len(cuda_visible_devices.split(\",\"))\n",
"\n",
"\n",
"def train_fn():\n",
" global num_processes\n",
" from datasets import Dataset, concatenate_datasets\n",
" import pandas as pd\n",
" from pathlib import Path\n",
" import random\n",
" from transformers import AutoTokenizer\n",
" import torch\n",
" import numpy as np\n",
" from transformers import AutoModelForTokenClassification\n",
" from transformers.data.data_collator import DataCollatorForTokenClassification\n",
" from transformers.training_args import TrainingArguments\n",
" from transformers.trainer import Trainer\n",
" from transformers.trainer_callback import TrainerCallback\n",
" import numpy as np\n",
" from sklearn.metrics import precision_recall_fscore_support\n",
" import os\n",
" import wandb\n",
"\n",
" num_epochs = int(os.getenv(\"NUM_EPOCHS\", 1))\n",
" output_dir = os.getenv(\"OUTPUT_DIR\", \"./model\")\n",
" seed = int(os.getenv(\"RANDOM_SEED\", 42))\n",
" model_id = os.getenv(\"MODEL_ID\")\n",
" hub_token = os.getenv(\"HF_TOKEN\")\n",
" save_steps = float(os.getenv(\"SAVE_STEPS\", 100))\n",
" if save_steps.is_integer():\n",
" save_steps = int(save_steps)\n",
" train_size = float(os.getenv(\"TRAIN_SIZE\", 12_000_000))\n",
" test_size = float(os.getenv(\"TEST_SIZE\", 1_200_000))\n",
" if train_size.is_integer():\n",
" train_size = int(train_size)\n",
" if test_size.is_integer():\n",
" test_size = int(test_size)\n",
" hidden_dropout_prob = float(os.getenv(\"HIDDEN_DROPOUT_PROB\", 0.14))\n",
" attention_probs_dropout_prob = float(os.getenv(\"ATTENTION_PROBS_DROPOUT_PROB\", 0.14))\n",
" frequency_exponent = float(os.getenv(\"FREQUENCY_EXPONENT\", 0.35))\n",
" gamma = float(os.getenv(\"GAMMA\", 2.1))\n",
" learning_rate = float(os.getenv(\"LEARNING_RATE\", 2.5e-5))\n",
" lr_scheduler_type = os.getenv(\"LR_SCHEDULER_TYPE\", \"cosine\")\n",
" weight_decay = float(os.getenv(\"WEIGHT_DECAY\", 0.007))\n",
" warmup_ratio = float(os.getenv(\"WARMUP_RATIO\", 0.03))\n",
" per_device_train_batch_size = int(os.getenv(\"PER_DEVICE_TRAIN_BATCH_SIZE\", 256))\n",
" max_saved_checkpoints = int(os.getenv(\"MAX_SAVED_CHECKPOINTS\", 8))\n",
" patience = max_saved_checkpoints - 1\n",
"\n",
" num_processes = torch.cuda.device_count()\n",
" \n",
" tokenizer = AutoTokenizer.from_pretrained(\"microsoft/deberta-v3-base\")\n",
" \n",
" data_dir = Path(\"data\")\n",
" output = Path(output_dir)\n",
" random.seed(seed)\n",
" torch.manual_seed(seed)\n",
" np.random.seed(seed)\n",
"\n",
" \n",
" label_list = [\n",
" \"O\",\n",
" \"B-CHA\",\n",
" \"I-CHA\",\n",
" \"B-LOC\",\n",
" \"I-LOC\",\n",
" \"B-FAC\",\n",
" \"I-FAC\",\n",
" \"B-OBJ\",\n",
" \"I-OBJ\",\n",
" \"B-EVT\",\n",
" \"I-EVT\",\n",
" \"B-ORG\",\n",
" \"I-ORG\",\n",
" \"B-MISC\",\n",
" \"I-MISC\"\n",
" ]\n",
" label_to_id = {label: i for i, label in enumerate(label_list)}\n",
" id_to_label = {i: label for i, label in enumerate(label_list)}\n",
"\n",
" datasets = []\n",
" for parquet_file in sorted(data_dir.glob(\"*.parquet\")):\n",
" ds = Dataset.from_parquet(str(parquet_file))\n",
" datasets.append(ds)\n",
"\n",
" full_ds = concatenate_datasets(datasets)\n",
" splits = full_ds.train_test_split(train_size=train_size, test_size=test_size, seed=seed)\n",
"\n",
" train_ds = splits['train']\n",
" eval_ds = splits['test']\n",
"\n",
" stats_file = \"label_counts.csv\"\n",
" stats_df = pd.read_csv(stats_file)\n",
" stats_df.head()\n",
"\n",
" total_count = stats_df[\"total\"].sum()\n",
" label_frequencies = {\n",
" label: stats_df[label].sum() / total_count for label in label_list\n",
" }\n",
" \n",
" label_weights = {}\n",
" for label, freq in label_frequencies.items():\n",
" label_weights[label] = 1.0 / freq ** frequency_exponent\n",
"\n",
" weight_tensor = torch.tensor([label_weights[label] for label in label_list], dtype=torch.float32)\n",
"\n",
" model = AutoModelForTokenClassification.from_pretrained(\n",
" \"microsoft/deberta-v3-base\",\n",
" num_labels=len(label_list),\n",
" id2label=id_to_label,\n",
" label2id=label_to_id,\n",
" ignore_mismatched_sizes=True,\n",
" hidden_dropout_prob=hidden_dropout_prob,\n",
" attention_probs_dropout_prob=attention_probs_dropout_prob\n",
" )\n",
" \n",
" data_collator = DataCollatorForTokenClassification(\n",
" tokenizer=tokenizer,\n",
" padding=True\n",
" )\n",
"\n",
"\n",
" def create_compute_metrics_fn(eval_dataset):\n",
" \"\"\"\n",
" Factory function that creates a compute_metrics function with access to eval_dataset.\n",
" \"\"\"\n",
" def compute_metrics(eval_pred):\n",
" predictions, labels = eval_pred\n",
" predictions_raw = predictions # Keep raw predictions for logging\n",
" predictions = np.argmax(predictions, axis=2)\n",
" \n",
" # Remove ignored indices\n",
" true_predictions = [\n",
" [id_to_label[p] for (p, l) in zip(pred, label) if l != -100]\n",
" for pred, label in zip(predictions, labels)\n",
" ]\n",
" true_labels = [\n",
" [id_to_label[l] for (p, l) in zip(pred, label) if l != -100]\n",
" for pred, label in zip(predictions, labels)\n",
" ]\n",
" \n",
" # Flatten\n",
" all_predictions = [item for sublist in true_predictions for item in sublist]\n",
" all_labels = [item for sublist in true_labels for item in sublist]\n",
" \n",
" # Calculate metrics excluding 'O' class\n",
" entity_labels = [l for l in label_list if l != 'O']\n",
" \n",
" precision, recall, f1, support = precision_recall_fscore_support(\n",
" all_labels,\n",
" all_predictions,\n",
" labels=entity_labels,\n",
" average='weighted',\n",
" zero_division=0\n",
" )\n",
"\n",
" return {\n",
" 'entity_precision': precision,\n",
" 'entity_recall': recall,\n",
" 'entity_f1': f1,\n",
" }\n",
" \n",
" return compute_metrics\n",
"\n",
" # Create the compute_metrics function with access to eval_ds\n",
" compute_metrics = create_compute_metrics_fn(eval_ds)\n",
"\n",
" class FocalLoss(torch.nn.Module):\n",
" def __init__(self, alpha=None, gamma=2.0, reduction='mean', ignore_index=-100):\n",
" \"\"\"\n",
" alpha: class weights tensor\n",
" gamma: focusing parameter (higher = more focus on hard examples)\n",
" ignore_index: label to ignore (for padding tokens)\n",
" \"\"\"\n",
" super().__init__()\n",
" self.alpha = alpha\n",
" self.gamma = gamma\n",
" self.reduction = reduction\n",
" self.ignore_index = ignore_index\n",
" \n",
" def forward(self, logits, labels):\n",
" # logits shape: (batch_size, seq_len, num_classes)\n",
" # labels shape: (batch_size, seq_len)\n",
" \n",
" # Reshape for loss calculation\n",
" logits_flat = logits.view(-1, logits.size(-1)) # (batch*seq_len, num_classes)\n",
" labels_flat = labels.view(-1) # (batch*seq_len)\n",
" \n",
" # Calculate cross entropy (without reduction)\n",
" ce_loss = torch.nn.functional.cross_entropy(\n",
" logits_flat, \n",
" labels_flat, \n",
" reduction='none',\n",
" ignore_index=self.ignore_index\n",
" )\n",
" \n",
" # Get the probabilities for the correct class\n",
" p = torch.exp(-ce_loss)\n",
" \n",
" # Calculate focal term: (1 - p)^gamma\n",
" focal_term = (1 - p) ** self.gamma\n",
" \n",
" # Apply focal term to loss\n",
" focal_loss = focal_term * ce_loss\n",
" \n",
" # Apply class weights if provided\n",
" if self.alpha is not None:\n",
" # Create a mask for valid (non-ignored) tokens\n",
" valid_mask = labels_flat != self.ignore_index\n",
" \n",
" # Gather the weights for each sample's true class\n",
" # Only for valid labels to avoid index errors\n",
" valid_labels = labels_flat.clone()\n",
" valid_labels[~valid_mask] = 0 # Set ignored labels to 0 to avoid index errors\n",
" \n",
" alpha_t = self.alpha.gather(0, valid_labels)\n",
" # Apply mask to weights\n",
" alpha_t = alpha_t * valid_mask.float()\n",
" \n",
" focal_loss = alpha_t * focal_loss\n",
" \n",
" # Apply reduction\n",
" if self.reduction == 'mean':\n",
" # Only average over non-ignored tokens\n",
" valid_tokens = (labels_flat != self.ignore_index).sum()\n",
" return focal_loss.sum() / valid_tokens.clamp(min=1)\n",
" elif self.reduction == 'sum':\n",
" return focal_loss.sum()\n",
" else:\n",
" return focal_loss\n",
" \n",
" class FocalLossTrainer(Trainer):\n",
" def __init__(self, *args, class_weights=None, gamma=2.0, **kwargs):\n",
" super().__init__(*args, **kwargs)\n",
" self.class_weights = class_weights\n",
" self.gamma = gamma\n",
" \n",
" def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):\n",
" \"\"\"\n",
" Override compute_loss to use focal loss.\n",
" num_items_in_batch parameter added for compatibility with newer transformers versions.\n",
" \"\"\"\n",
" labels = inputs.get(\"labels\")\n",
" outputs = model(**inputs)\n",
" logits = outputs.get(\"logits\")\n",
" \n",
" # Move weights to the same device as logits\n",
" if self.class_weights is not None:\n",
" weights = self.class_weights.to(logits.device)\n",
" else:\n",
" weights = None\n",
" \n",
" # Initialize focal loss\n",
" loss_fct = FocalLoss(\n",
" alpha=weights,\n",
" gamma=self.gamma,\n",
" ignore_index=-100\n",
" )\n",
" \n",
" # Calculate loss\n",
" loss = loss_fct(logits, labels)\n",
" \n",
" return (loss, outputs) if return_outputs else loss\n",
"\n",
" \n",
"\n",
" training_args = TrainingArguments(\n",
" output_dir=str(output),\n",
" learning_rate=learning_rate,\n",
" lr_scheduler_type=lr_scheduler_type,\n",
" per_device_train_batch_size=per_device_train_batch_size,\n",
" weight_decay=weight_decay,\n",
" warmup_ratio=warmup_ratio,\n",
" gradient_accumulation_steps=1,\n",
" logging_steps=50,\n",
" num_train_epochs=num_epochs,\n",
" save_strategy=\"steps\",\n",
" save_steps=save_steps,\n",
" save_total_limit=3,\n",
" eval_strategy=\"steps\",\n",
" eval_steps=save_steps,\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"eval_entity_f1\",\n",
" greater_is_better=True,\n",
" bf16=True,\n",
" tf32=True,\n",
" report_to='wandb',\n",
" run_name=run_name,\n",
" push_to_hub=True,\n",
" hub_strategy=\"checkpoint\",\n",
" hub_token=hub_token,\n",
" dataloader_persistent_workers=True,\n",
" dataloader_num_workers=2,\n",
" dataloader_pin_memory=True,\n",
" ddp_find_unused_parameters=False,\n",
" gradient_checkpointing=False,\n",
" hub_model_id=model_id,\n",
" hub_private_repo=True\n",
" )\n",
"\n",
" class CustomEarlyStoppingCallback(TrainerCallback):\n",
" def __init__(self, patience=2, threshold=0.001):\n",
" self.patience = patience\n",
" self.threshold = threshold\n",
" self.best_metric = None\n",
" self.wait = 0\n",
" \n",
" def on_evaluate(self, args, state, control, metrics=None, **kwargs):\n",
" if metrics is None or \"eval_entity_f1\" not in metrics:\n",
" return control\n",
" metric_value = metrics.get(\"eval_entity_f1\")\n",
" \n",
" if self.best_metric is None:\n",
" self.best_metric = metric_value\n",
" elif metric_value > self.best_metric + self.threshold:\n",
" self.best_metric = metric_value\n",
" self.wait = 0\n",
" else:\n",
" self.wait += 1\n",
" if self.wait >= self.patience:\n",
" control.should_training_stop = True\n",
" print(f\"Early stopping triggered. Best F1: {self.best_metric:.4f}\")\n",
" \n",
" return control\n",
" \n",
"\n",
" trainer = FocalLossTrainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=train_ds,\n",
" eval_dataset=eval_ds,\n",
" processing_class=tokenizer,\n",
" data_collator=data_collator,\n",
" compute_metrics=compute_metrics,\n",
" class_weights=weight_tensor,\n",
" gamma=gamma,\n",
" callbacks=[CustomEarlyStoppingCallback(patience=patience, threshold=0.0001)]\n",
" )\n",
" \n",
" if wandb.run is not None:\n",
" # Add custom config values\n",
" wandb.config.update({\n",
" # Data configuration\n",
" \"train_samples\": len(train_ds),\n",
" \"eval_samples\": len(eval_ds),\n",
" \"train_size_requested\": train_size,\n",
" \"test_size_requested\": test_size,\n",
" \"actual_train_size\": len(train_ds),\n",
" \"actual_eval_size\": len(eval_ds),\n",
"\n",
" # Model architecture details\n",
" \"model_architecture\": \"deberta-v3-base\",\n",
" \"num_labels\": len(label_list),\n",
" \"label_list\": label_list,\n",
"\n",
" # Loss function configuration\n",
" \"loss_function\": \"focal_loss\",\n",
" \"focal_gamma\": gamma,\n",
" \"focal_alpha\": \"weighted\",\n",
" \"frequency_exponent\": frequency_exponent,\n",
"\n",
" # Dropout configuration\n",
" \"hidden_dropout_prob\": hidden_dropout_prob,\n",
" \"attention_probs_dropout_prob\": attention_probs_dropout_prob,\n",
"\n",
" # Training configuration not in TrainingArguments\n",
" \"max_saved_checkpoints\": max_saved_checkpoints,\n",
" \"early_stopping_patience\": patience,\n",
" \"early_stopping_threshold\": 0.001,\n",
"\n",
" # Environment info\n",
" \"cuda_devices\": cuda_visible_devices,\n",
" \"num_gpus\": num_devices,\n",
"\n",
" # Data processing\n",
" \"tokenizer\": \"microsoft/deberta-v3-base\"\n",
"\n",
" # Experiment metadata\n",
" \"experiment_type\": \"ner_fiction\",\n",
" \"data_source\": \"gutenberg_ao3_mixed\",\n",
" \"random_seed\": seed,\n",
"\n",
" # Logging configuration\n",
" \"n_eval_samples\": n_eval_samples,\n",
" \"log_predictions_to_wandb\": log_predictions_to_wandb,\n",
" })\n",
"\n",
" has_checkpoints = bool([f for f in os.scandir(output_dir) if f.is_dir() and \"checkpoint\" in f.name])\n",
" if has_checkpoints:\n",
" trainer.train(resume_from_checkpoint=True)\n",
" else:\n",
" trainer.train()\n",
"\n",
"notebook_launcher(train_fn, num_processes=num_devices)"
]
}
],
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|