Named Entity Recognition (NER) Implementation Approaches: LSTM vs. BERT in Microsoft AI-For-Beginners

The Microsoft AI-For-Beginners repository implements two primary approaches for Named Entity Recognition: a classic LSTM-based sequence model built with TensorFlow/Keras, and a transformer-based approach using pre-trained BERT from Hugging Face.

The microsoft/AI-For-Beginners repository provides hands-on implementations for NER in its natural language processing curriculum. Located in lessons/5-NLP/19-NER/, the materials demonstrate how modern deep learning architectures solve token classification tasks. This guide examines both implementation approaches with complete, runnable code examples from the source.

Approach 1: LSTM-Based NER with TensorFlow/Keras

The repository's primary implementation uses recurrent neural networks with LSTM layers to predict entity tags sequence-to-sequence. This classical approach teaches foundational concepts before introducing transformers.

Architecture Overview

The model in lessons/5-NLP/19-NER/NER-TF.ipynb stacks:

• Embedding layer for dense token representations
• Bidirectional LSTM for context-aware encoding
• Dense output layer with softmax for tag prediction

Core Implementation

import tensorflow as tf
from tensorflow.keras import layers, Model

# Build LSTM-based NER model as implemented in NER-TF.ipynb

def build_lstm_ner_model(vocab_size, embedding_dim, lstm_units, num_tags):
    """
    Constructs the LSTM NER model from lessons/5-NLP/19-NER/NER-TF.ipynb
    
    Parameters match the notebook's architecture for CoNLL-2003 style data.
    """
    inputs = layers.Input(shape=(None,), dtype='int32', name='token_ids')
    
    # Token embeddings - converts word indices to dense vectors

    x = layers.Embedding(
        input_dim=vocab_size,
        output_dim=embedding_dim,
        mask_zero=True,  # Handles variable-length sequences

        name='token_embedding'
    )(inputs)
    
    # Bidirectional LSTM captures left-to-right and right-to-left context

    x = layers.Bidirectional(
        layers.LSTM(lstm_units, return_sequences=True),
        name='bilstm_encoder'
    )(x)
    
    # Output: probability distribution over NER tags for each token

    outputs = layers.Dense(num_tags, activation='softmax', name='ner_classifier')(x)
    
    model = Model(inputs, outputs, name='lstm_ner')
    return model

# Example usage matching the notebook parameters

vocab_size = 20000      # Size of token vocabulary

embedding_dim = 100     # Dimension of token embeddings

lstm_units = 128        # Hidden units in each LSTM direction

num_tags = 9            # BIO tags: O, B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG, B-MISC, I-MISC

model = build_lstm_ner_model(vocab_size, embedding_dim, lstm_units, num_tags)
model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

Data Format and BIO Tagging

The LSTM implementation expects BIO-encoded sequences (Beginning, Inside, Outside). The repository uses the CoNLL-2003 dataset format where each token maps to a tag:

# Sample data structure from ner_dataset.csv referenced in the notebook

# Token       POS    Chunk    NER_tag

# ------------------------------------

# EU          NNP    B-NP     B-ORG

# rejects     VBZ    B-VP     O

# German      JJ     B-NP     B-MISC

# call        NN     I-NP     O

# to          TO     B-VP     O

# boycott     VB     I-VP     O

# British     JJ     B-NP     B-MISC

# lamb        NN     I-NP     O

Training Configuration

# Training parameters from NER-TF.ipynb

history = model.fit(
    x_train,           # Padded token sequences (batch_size, max_len)

    y_train,           # Padded tag indices (batch_size, max_len)

    validation_split=0.1,
    batch_size=32,
    epochs=5,
    callbacks=[
        tf.keras.callbacks.EarlyStopping(patience=2, restore_best_weights=True)
    ]
)

Approach 2: Transformer-Based NER with BERT

The notebook references a production-grade implementation using Hugging Face transformers. This approach leverages pre-trained contextual representations rather than training embeddings from scratch.

Why BERT for NER

• Pre-trained context: BERT captures semantic relationships from billions of tokens
• Fine-tuning efficiency: Requires less task-specific data than LSTM training
• State-of-the-art accuracy: Contextualized representations handle polysemy and ambiguous boundaries

Implementation with Hugging Face

The notebook points to Hugging Face's NER tutorial. Below is the equivalent implementation using the same patterns:

from transformers import (
    AutoTokenizer,
    AutoModelForTokenClassification,
    TrainingArguments,
    Trainer,
    DataCollatorForTokenClassification
)
from datasets import load_dataset
import numpy as np

# BERT-based NER implementation matching the Hugging Face approach

class BERT_NER_Pipeline:
    """
    Fine-tunes BERT for token classification as referenced in NER-TF.ipynb.
    Uses bert-base-cased (cased preserves entity capitalization patterns).
    """
    
    def __init__(self, model_name='bert-base-cased', num_labels=9):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForTokenClassification.from_pretrained(
            model_name,
            num_labels=num_labels
        )
        self.label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 
                          'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']
    
    def tokenize_and_align_labels(self, examples):
        """
        Tokenizes words and aligns NER labels with WordPiece subwords.
        Critical: -100 label ignores subword tokens after the first in a word.
        """
        tokenized_inputs = self.tokenizer(
            examples['tokens'],
            truncation=True,
            is_split_into_words=True,
            padding='max_length',
            max_length=128
        )
        
        labels = []
        for i, label in enumerate(examples['ner_tags']):
            word_ids = tokenized_inputs.word_ids(batch_index=i)
            previous_word_idx = None
            label_ids = []
            
            for word_idx in word_ids:
                if word_idx is None:
                    # Special tokens ([CLS], [SEP], [PAD]) get -100 (ignored)

                    label_ids.append(-100)
                elif word_idx != previous_word_idx:
                    # First subword of a word gets the actual label

                    label_ids.append(label[word_idx])
                else:
                    # Subsequent subwords are ignored in loss computation

                    label_ids.append(-100)
                previous_word_idx = word_idx
            
            labels.append(label_ids)
        
        tokenized_inputs['labels'] = labels
        return tokenized_inputs
    
    def compute_metrics(self, eval_pred):
        """Evaluates precision, recall, F1 excluding -100 (ignored) labels."""
        predictions, labels = eval_pred
        predictions = np.argmax(predictions, axis=2)
        
        true_predictions = [
            self.label_list[p] 
            for pred, label in zip(predictions, labels) 
            for p, l in zip(pred, label) if l != -100
        ]
        true_labels = [
            self.label_list[l] 
            for label in labels 
            for l in label if l != -100
        ]
        
        from seqeval.metrics import classification_report
        return classification_report(true_labels, true_predictions, output_dict=True)

# Training setup

def train_bert_ner():
    """Minimal training loop equivalent to the Hugging Face tutorial."""
    # Load CoNLL-2003 or compatible dataset

    dataset = load_dataset('conll2003')  # Or custom dataset matching repo format

    
    pipeline = BERT_NER_Pipeline()
    
    # Preprocess: tokenize and align labels

    tokenized_dataset = dataset.map(
        pipeline.tokenize_and_align_labels,
        batched=True,
        remove_columns=dataset['train'].column_names
    )
    
    # Training configuration

    training_args = TrainingArguments(
        output_dir='./bert-ner-results',
        evaluation_strategy='epoch',
        learning_rate=5e-5,
        per_device_train_batch_size=16,
        per_device_eval_batch_size=16,
        num_train_epochs=3,
        weight_decay=0.01,
        save_total_limit=2,
        load_best_model_at_end=True,
        metric_for_best_model='f1'
    )
    
    trainer = Trainer(
        model=pipeline.model,
        args=training_args,
        train_dataset=tokenized_dataset['train'],
        eval_dataset=tokenized_dataset['validation'],
        tokenizer=pipeline.tokenizer,
        data_collator=DataCollatorForTokenClassification(pipeline.tokenizer),
        compute_metrics=pipeline.compute_metrics
    )
    
    trainer.train()
    return trainer

Hardware Considerations

Approach	VRAM Requirement	Training Time	Inference Speed
LSTM (128 units)	~2 GB	~30 min/epoch (CPU)	Fast
BERT-base	~8-12 GB	~2 hrs/epoch (GPU)	Moderate

Comparing the Two Approaches

LSTM-based NER (lessons/5-NLP/19-NER/NER-TF.ipynb) excels when:

• Teaching fundamental sequence modeling concepts
• Deploying to resource-constrained environments
• Working with domain-specific vocabularies without pre-trained embeddings

BERT-based NER (Hugging Face tutorial) excels when:

• Maximum accuracy is required on standard entity types
• Limited labeled data is available (transfer learning from pre-training)
• Downstream integration with other NLP pipelines

Performance Characteristics

According to standard benchmarks referenced in the repository materials:

• LSTM with GloVe embeddings: ~85% F1 on CoNLL-2003
• BERT-base fine-tuned: ~91-92% F1 on CoNLL-2003

Key Files and Resources

• lessons/5-NLP/19-NER/NER-TF.ipynb — Complete LSTM implementation with data loading, model training, and evaluation
• CoNLL-2003 dataset — Standard NER benchmark used by the notebook (download via Kaggle)
• Hugging Face NER Course — BERT fine-tuning tutorial referenced in notebook comments

Summary

• Two validated approaches: The Microsoft AI-For-Beginners repository demonstrates both classical (LSTM) and modern (BERT) NER implementations
• LSTM implementation: Found in NER-TF.ipynb, uses bidirectional LSTM with embedding layers, trains from scratch on BIO-tagged data
• BERT implementation: Referenced via Hugging Face, leverages AutoModelForTokenClassification with label alignment for WordPiece tokenization
• Key technical detail: BERT requires careful label alignment (-100 for ignored subwords) while LSTM uses simple sequence-to-sequence mapping
• Learning progression: The repository intentionally presents LSTM first to build intuition before introducing transformer complexity

Frequently Asked Questions

What dataset does the LSTM notebook use?

The NER-TF.ipynb notebook references the CoNLL-2003 English dataset, a standard benchmark containing news articles annotated with person, organization, location, and miscellaneous entities. The data follows BIO tagging format with tab-separated columns for tokens, part-of-speech tags, chunk tags, and NER labels. Download links are typically provided within the notebook or via the repository's data specifications.

Why does BERT need label alignment while LSTM does not?

BERT uses WordPiece tokenization, which splits rare words into subwords (e.g., "playing" → "play", "##ing"). Since NER labels apply to whole words, not subwords, the Hugging Face implementation assigns -100 to all subwords after the first so they don't contribute to loss. LSTM implementations typically use whole-word vocabulary indices, maintaining a 1:1 token-label correspondence without subword splitting.

Can I run the LSTM notebook without a GPU?

Yes, the LSTM model trains efficiently on CPU due to its modest parameter count (~2-5 million). The bidirectional LSTM in NER-TF.ipynb uses 128-dimensional hidden states, which modern CPUs handle in reasonable time (minutes per epoch for small datasets). BERT fine-tuning practically requires GPU acceleration—expect 10-50x slower training on CPU with significantly higher memory requirements.

How do I extend these approaches to custom entity types?

Modify the num_tags or num_labels parameter to match your tag inventory. For the LSTM approach in lessons/5-NLP/19-NER/NER-TF.ipynb, change the final Dense layer output dimension. For BERT, pass your custom id2label and label2id mappings to AutoModelForTokenClassification.from_pretrained(). Ensure your training data uses consistent BIO encoding for the new entity types.

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