How to Build Language Models and Train Custom Embeddings: A Complete Guide with Microsoft's AI-For-Beginners

Build language models and train custom embeddings from scratch using PyTorch or TensorFlow with Microsoft's open-source curriculum, which provides end-to-end notebooks covering GPT-style transformers, CBoW word vectors, and complete training pipelines.

This guide walks you through the exact implementation patterns found in the microsoft/AI-For-Beginners repository. You'll learn how to construct transformer-based language models, implement trainable embedding layers, and execute full training loops—using code directly extracted from the official educational notebooks.

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Data Preparation for Language Modeling

Before training any model, you need clean, tokenized text sequences. The repository demonstrates this in lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb with a sliding-window tokenization approach.

Key preprocessing steps include:

• Lower-casing and punctuation removal
• Building a vocabulary-to-integer mapping
• Creating input-target pairs using a sliding context window

The torch.utils.data.Dataset implementation handles batching and sequence padding automatically. Each sample consists of input_ids (context tokens) and target_ids (next token to predict).

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Building Custom Embedding Layers

Embeddings are the foundation of every neural language model. The microsoft/AI-For-Beginners repository dedicates lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb to this topic.

PyTorch Implementation

import torch.nn as nn

# V × d trainable lookup table

embedding_layer = nn.Embedding(num_embeddings=vocab_size, embedding_dim=256)

The nn.Embedding layer initializes a matrix of shape (vocab_size, embed_dim) with small random values. During backpropagation, gradients flow directly into this matrix, updating word representations based on prediction error.

TensorFlow Equivalent

from tensorflow.keras import layers

embedding_layer = layers.Embedding(input_dim=vocab_size, output_dim=256)

As shown in lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb, both frameworks implement the same mathematical operation: a sparse lookup that converts token IDs into dense vectors.

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Constructing a GPT-Style Language Model

The core language modeling implementation lives in lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb. This notebook builds a causal (autoregressive) transformer that predicts the next token given all previous tokens.

Model Architecture

import torch
import torch.nn as nn

class SimpleGPT(nn.Module):
    def __init__(self, vocab_size, embed_dim, n_heads, n_layers, max_len=512):
        super().__init__()
        self.token_emb = nn.Embedding(vocab_size, embed_dim)
        self.pos_emb = nn.Parameter(torch.randn(1, max_len, embed_dim))
        
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=embed_dim,
            nhead=n_heads,
            dim_feedforward=4 * embed_dim,
            activation='gelu'
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=n_layers)
        self.proj = nn.Linear(embed_dim, vocab_size)

    def forward(self, x):
        seq_len = x.size(1)
        token_embeddings = self.token_emb(x)
        position_embeddings = self.pos_emb[:, :seq_len, :]
        x = token_embeddings + position_embeddings
        x = self.transformer(x)
        logits = self.proj(x)
        return logits

Key architectural decisions:

• Learnable positional embeddings rather than sinusoidal encodings (simplified for educational clarity)
• Causal masking handled internally by the transformer encoder when configured with is_causal=True
• GELU activation following the original GPT-2 design

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Training Loop and Optimization

The training configuration in GPT-PyTorch.ipynb uses AdamW optimization with gradient clipping for stability.

model = SimpleGPT(vocab_size=10000, embed_dim=256, n_heads=4, n_layers=2)
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
criterion = nn.CrossEntropyLoss()

for epoch in range(num_epochs):
    for batch in dataloader:
        optimizer.zero_grad()
        logits = model(batch['input_ids'])
        loss = criterion(
            logits.view(-1, logits.size(-1)),
            batch['target_ids'].view(-1)
        )
        loss.backward()
        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
        optimizer.step()

Critical training details:

• The transformer predicts every position simultaneously, so targets are shifted by one position
• Cross-entropy is computed across the full vocabulary at each sequence position
• Gradient clipping at norm 1.0 prevents exploding gradients on small datasets

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Training Custom Word Embeddings with CBoW

For pure embedding training without a full language model, the repository provides lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb. This implements the Continuous-Bag-of-Words architecture that learns embeddings by predicting a target word from its surrounding context.

CBoW Model Definition

class CBoW(nn.Module):
    def __init__(self, vocab_size, embed_dim):
        super().__init__()
        self.emb = nn.EmbeddingBag(vocab_size, embed_dim, mode='mean')
        self.fc = nn.Linear(embed_dim, vocab_size)

    def forward(self, context_words, offsets):
        embed = self.emb(context_words, offsets)
        out = self.fc(embed)
        return out

The nn.EmbeddingBag layer efficiently computes the mean of context embeddings in a single operation, avoiding explicit averaging in Python.

CBoW Training Loop

model = CBoW(vocab_size=5000, embed_dim=128)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
criterion = nn.CrossEntropyLoss()

for epoch in range(epochs):
    for ctx, offsets, target in cbow_loader:
        optimizer.zero_grad()
        logits = model(ctx, offsets)
        loss = criterion(logits, target)
        loss.backward()
        optimizer.step()

Why SGD for CBoW? The repository uses stochastic gradient descent rather than Adam because CBoW trains on word co-occurrence statistics where aggressive adaptive optimization can destabilize the embedding geometry.

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TensorFlow Implementation Parity

The microsoft/AI-For-Beginners repository maintains framework-agnostic coverage. lessons/5-NLP/20-LangModels/GPT-TensorFlow.ipynb mirrors the PyTorch implementation using Keras.

import tensorflow as tf
from tensorflow.keras import layers

class TF_GPT(tf.keras.Model):
    def __init__(self, vocab_size, embed_dim, num_heads, num_layers, max_len=512):
        super().__init__()
        self.token_emb = layers.Embedding(vocab_size, embed_dim)
        self.pos_emb = self.add_weight(
            "pos_emb",
            shape=(1, max_len, embed_dim),
            initializer="random_normal"
        )
        self.transformer = [
            layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
            for _ in range(num_layers)
        ]
        self.dense = layers.Dense(vocab_size)

    def call(self, x):
        seq_len = tf.shape(x)[1]
        x = self.token_emb(x) + self.pos_emb[:, :seq_len, :]
        for mha in self.transformer:
            attn_output = mha(x, x, attention_mask=self._causal_mask(seq_len))
            x = layers.Add()([x, attn_output])
        return self.dense(x)

    def _causal_mask(self, seq_len):
        i = tf.range(seq_len)[:, None]
        j = tf.range(seq_len)
        return tf.cast(i >= j, tf.float32)[None, None, :, :]

Notable differences from PyTorch:

• Explicit causal mask construction in _causal_mask
• MultiHeadAttention layers applied sequentially rather than stacked in a transformer encoder
• Residual connections implemented manually with layers.Add

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Generation and Inference

After training, language models generate text via sequential sampling. The repository demonstrates temperature-controlled sampling where the softmax temperature parameter controls randomness:

• Temperature → 0: Greedy decoding (always select highest probability token)
• Temperature = 1.0: True distribution sampling
• Temperature > 1.0: More random, diverse outputs

Generation proceeds by feeding predicted tokens back as input for the next step—implemented in the inference sections of both GPT-PyTorch.ipynb and GPT-TensorFlow.ipynb.

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Key Files and Notebooks

File Path	Purpose
lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb	Complete GPT-style language model with training and generation
lessons/5-NLP/20-LangModels/GPT-TensorFlow.ipynb	TensorFlow equivalent of the above
lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb	Detailed embedding layer tutorial and visualization
lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb	TensorFlow embedding implementation
lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb	Standalone word embedding training with CBoW
lessons/5-NLP/16-RNN/RNNPyTorch.ipynb	RNN-based language modeling for comparison

All notebooks are executable end-to-end and include narrative explanations of the mathematical concepts underlying each implementation.

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Summary

• Build language models using transformer decoders with causal masking, as implemented in GPT-PyTorch.ipynb and GPT-TensorFlow.ipynb
• Train custom embeddings either jointly with language models via nn.Embedding or separately using CBoW with nn.EmbeddingBag
• Use AdamW with gradient clipping for transformer training; SGD suffices for standalone embedding objectives
• Choose your framework: The repository provides equivalent PyTorch and TensorFlow implementations for every concept
• Start with provided notebooks: Each file in lessons/5-NLP/ is self-contained and runnable

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Frequently Asked Questions

What is the difference between training embeddings with a language model versus CBoW?

Language model training updates embeddings based on next-token prediction across full sequences, capturing syntactic and long-range semantic patterns. CBoW training updates embeddings based on local co-occurrence statistics, producing vectors where geometric similarity corresponds to contextual interchangeability. According to the microsoft/AI-For-Beginners source code, language model embeddings generally perform better on downstream tasks but require more computation.

How does the nn.EmbeddingBag layer in PyTorch improve CBoW training?

The nn.EmbeddingBag layer combines the embedding lookup and averaging operations into a single optimized kernel, as shown in CBoW-PyTorch.ipynb. This reduces memory bandwidth and eliminates temporary tensor creation, yielding significant speedups when processing variable-length context windows. The offsets parameter indicates where each example's context begins in the flattened input tensor.

Why does the GPT implementation use learnable positional embeddings instead of sinusoidal encodings?

The SimpleGPT class in GPT-PyTorch.ipynb uses nn.Parameter(torch.randn(...)) for simplicity and educational clarity. The repository notes that sinusoidal encodings from "Attention Is All You Need" are theoretically appealing but learnable embeddings perform comparably in practice and are easier for beginners to understand. Production implementations often use rotary positional embeddings (RoPE) or ALiBi instead.

Can these notebooks scale to larger datasets and model sizes?

The microsoft/AI-For-Beginners implementations are intentionally minimal for educational purposes. To scale, you would need distributed training (DataParallel or DistributedDataParallel in PyTorch), mixed-precision training, and gradient accumulation—techniques not included in the base notebooks. The architecture patterns, however, remain valid; the repository's GPT-PyTorch.ipynb explicitly notes where production implementations would diverge.

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