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Training Script for NER Model
Fine-tunes DistilBERT on document entity extraction
Supports CORD and FUNSD datasets with BIO tagging
"""
import json
import logging
import os
from typing import Dict, List, Tuple
import numpy as np
import torch
import torch.nn as nn
from seqeval.metrics import (
classification_report,
f1_score,
precision_score,
recall_score,
)
from sklearn.model_selection import train_test_split
from torch.optim import AdamW
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
from transformers import AutoTokenizer, get_linear_schedule_with_warmup
from dataset_loader import UnifiedDatasetLoader
from ner_model import DocumentNERModel
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class NERDataset(Dataset):
"""PyTorch Dataset for NER"""
def __init__(self, tokenized_inputs: Dict, labels: List[List[int]]):
self.input_ids = tokenized_inputs["input_ids"]
self.attention_mask = tokenized_inputs["attention_mask"]
self.labels = labels
def __len__(self):
return len(self.input_ids)
def __getitem__(self, idx):
return {
"input_ids": torch.tensor(self.input_ids[idx], dtype=torch.long),
"attention_mask": torch.tensor(self.attention_mask[idx], dtype=torch.long),
"labels": torch.tensor(self.labels[idx], dtype=torch.long),
}
class NERDataPreprocessor:
"""Preprocess NER data with proper tokenization and label alignment"""
def __init__(self, tokenizer, label2id: Dict, max_length: int = 512):
self.tokenizer = tokenizer
self.label2id = label2id
self.max_length = max_length
def tokenize_and_align_labels(
self, examples: List[Dict]
) -> Tuple[Dict, List[List[int]]]:
"""
Tokenize texts and align labels with subword tokens
Args:
examples: List of dicts with 'tokens' and 'labels'
Returns:
Tuple of (tokenized_inputs, aligned_labels)
"""
tokenized_inputs = {"input_ids": [], "attention_mask": []}
aligned_labels = []
for example in examples:
tokens = example["tokens"]
labels = example["labels"]
# Tokenize with word boundaries
encoding = self.tokenizer(
tokens,
is_split_into_words=True,
max_length=self.max_length,
padding="max_length",
truncation=True,
return_tensors=None,
)
# Align labels
word_ids = encoding.word_ids()
label_ids = []
previous_word_idx = None
for word_idx in word_ids:
if word_idx is None:
# Special token (CLS, SEP, PAD)
label_ids.append(-100)
elif word_idx != previous_word_idx:
# First subword of a word
if word_idx < len(labels):
label = labels[word_idx]
label_ids.append(self.label2id.get(label, self.label2id["O"]))
else:
label_ids.append(-100)
else:
# Continuation of previous word (subword)
# Use same label or -100
if word_idx < len(labels):
label = labels[word_idx]
# Convert B- to I- for subwords
if label.startswith("B-"):
label = "I-" + label[2:]
label_ids.append(self.label2id.get(label, self.label2id["O"]))
else:
label_ids.append(-100)
previous_word_idx = word_idx
tokenized_inputs["input_ids"].append(encoding["input_ids"])
tokenized_inputs["attention_mask"].append(encoding["attention_mask"])
aligned_labels.append(label_ids)
return tokenized_inputs, aligned_labels
class NERTrainer:
"""Trainer for NER model"""
def __init__(
self,
model_name: str = "distilbert-base-uncased",
output_dir: str = "models/ner",
device: str = None,
):
self.model_name = model_name
self.output_dir = output_dir
self.device = (
device if device else ("cuda" if torch.cuda.is_available() else "cpu")
)
os.makedirs(output_dir, exist_ok=True)
logger.info(f"NER Trainer initialized on device: {self.device}")
if self.device == "cuda":
logger.info(f"Using GPU: {torch.cuda.get_device_name(0)}")
def prepare_data(self, test_size: float = 0.1, val_size: float = 0.1):
"""Load and prepare NER datasets"""
logger.info("Loading NER datasets...")
# Load data
loader = UnifiedDatasetLoader()
train_data = loader.load_ner_dataset(
datasets=["cord", "sroie", "funsd"], split="train"
)
# Get label mappings
mappings = loader.get_label_mappings()
self.label2id = mappings["ner"]
self.id2label = mappings["ner_id2label"]
logger.info(f"Loaded {len(train_data)} examples")
logger.info(f"Number of labels: {len(self.label2id)}")
# Split data
train_examples, temp_examples = train_test_split(
train_data, test_size=(test_size + val_size), random_state=42
)
val_examples, test_examples = train_test_split(
temp_examples, test_size=test_size / (test_size + val_size), random_state=42
)
logger.info(
f"Train: {len(train_examples)}, Val: {len(val_examples)}, Test: {len(test_examples)}"
)
return train_examples, val_examples, test_examples
def train(
self,
train_examples: List[Dict],
val_examples: List[Dict],
num_epochs: int = 25,
batch_size: int = 8,
learning_rate: float = 3e-5,
warmup_ratio: float = 0.1,
early_stopping_patience: int = 5,
):
"""Train the NER model"""
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(self.model_name)
model = DocumentNERModel(
model_name=self.model_name, num_labels=len(self.label2id)
)
model.to(self.device)
# Preprocess data
preprocessor = NERDataPreprocessor(tokenizer, self.label2id)
logger.info("Preprocessing training data...")
train_inputs, train_labels = preprocessor.tokenize_and_align_labels(
train_examples
)
train_dataset = NERDataset(train_inputs, train_labels)
logger.info("Preprocessing validation data...")
val_inputs, val_labels = preprocessor.tokenize_and_align_labels(val_examples)
val_dataset = NERDataset(val_inputs, val_labels)
# Create dataloaders
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size)
# Optimizer and scheduler
optimizer = AdamW(model.parameters(), lr=learning_rate)
total_steps = len(train_loader) * num_epochs
warmup_steps = int(total_steps * warmup_ratio)
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps
)
# Training loop
best_val_f1 = 0
patience_counter = 0
for epoch in range(num_epochs):
logger.info(f"\nEpoch {epoch + 1}/{num_epochs}")
# Training
model.train()
train_loss = 0
progress_bar = tqdm(train_loader, desc="Training")
for batch in progress_bar:
optimizer.zero_grad()
input_ids = batch["input_ids"].to(self.device)
attention_mask = batch["attention_mask"].to(self.device)
labels = batch["labels"].to(self.device)
outputs = model(input_ids, attention_mask, labels)
loss = outputs["loss"]
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
scheduler.step()
train_loss += loss.item()
progress_bar.set_postfix({"loss": loss.item()})
avg_train_loss = train_loss / len(train_loader)
logger.info(f"Train Loss: {avg_train_loss:.4f}")
# Validation
model.eval()
val_loss = 0
all_preds = []
all_labels = []
with torch.no_grad():
for batch in tqdm(val_loader, desc="Validation"):
input_ids = batch["input_ids"].to(self.device)
attention_mask = batch["attention_mask"].to(self.device)
labels = batch["labels"].to(self.device)
outputs = model(input_ids, attention_mask, labels)
val_loss += outputs["loss"].item()
preds = torch.argmax(outputs["logits"], dim=-1)
# Collect predictions and labels (excluding -100)
for pred_seq, label_seq in zip(preds, labels):
pred_list = []
label_list = []
for p, l in zip(pred_seq, label_seq):
if l != -100:
pred_list.append(self.id2label[p.item()])
label_list.append(self.id2label[l.item()])
if pred_list:
all_preds.append(pred_list)
all_labels.append(label_list)
avg_val_loss = val_loss / len(val_loader)
# Calculate metrics using seqeval
val_f1 = f1_score(all_labels, all_preds)
val_precision = precision_score(all_labels, all_preds)
val_recall = recall_score(all_labels, all_preds)
logger.info(f"Val Loss: {avg_val_loss:.4f}")
logger.info(
f"Val F1: {val_f1:.4f}, Precision: {val_precision:.4f}, Recall: {val_recall:.4f}"
)
# Early stopping
if val_f1 > best_val_f1:
best_val_f1 = val_f1
patience_counter = 0
# Save best model
model_path = os.path.join(self.output_dir, "best_ner.pt")
torch.save(model.state_dict(), model_path)
logger.info(f"Saved best model with F1: {best_val_f1:.4f}")
else:
patience_counter += 1
if patience_counter >= early_stopping_patience:
logger.info(f"Early stopping triggered after {epoch + 1} epochs")
break
# Save final model and metadata
torch.save(model.state_dict(), os.path.join(self.output_dir, "final_ner.pt"))
metadata = {
"model_name": self.model_name,
"num_labels": len(self.label2id),
"label2id": self.label2id,
"id2label": self.id2label,
"best_val_f1": best_val_f1,
}
with open(os.path.join(self.output_dir, "metadata.json"), "w") as f:
json.dump(metadata, f, indent=2)
logger.info("NER training complete!")
return best_val_f1
if __name__ == "__main__":
# Training configuration
trainer = NERTrainer(model_name="distilbert-base-uncased", output_dir="models/ner")
# Prepare data
train_examples, val_examples, test_examples = trainer.prepare_data(
test_size=0.1, val_size=0.1
)
# Train model
best_f1 = trainer.train(
train_examples=train_examples,
val_examples=val_examples,
num_epochs=30,
batch_size=32,
learning_rate=3e-5,
early_stopping_patience=5,
)
print(f"\nBest validation F1: {best_f1:.4f}")
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