We will build a Regression Language Model (RLM), a model that predicts continuous numerical values directly from text sequences in this coding implementation. Instead of classifying or generating text, we focus on training a transformer-based architecture that learns quantitative relationships hidden within natural language descriptions. We start by generating synthetic text-to-number data, tokenizing it efficiently, and then train a lightweight Transformer encoder to map linguistic cues to real-valued targets. By the end, we not only understand how RLMs can be implemented from scratch but also visualize their learning behavior and test their generalization on unseen examples. Check out the FULL CODES here.
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
from collections import Counter
import re
torch.manual_seed(42)
np.random.seed(42)
print("🚀 Regression Language Model (RLM) Tutorial")
print("=" * 60)We begin by importing essential libraries, such as PyTorch, NumPy, and Matplotlib, to build and visualize our Regression Language Model. We set random seeds to ensure reproducibility and initialize the environment, thereby guaranteeing consistent results each time the tutorial is run. Check out the FULL CODES here.
def generate_synthetic_data(n_samples=2000):
"""Generate synthetic text-to-number regression data"""
templates = [
("The temperature is {} degrees", lambda x: x),
("I rate this {} out of ten", lambda x: x),
("The price is {} dollars", lambda x: x),
("Confidence level: {}", lambda x: x / 100),
("Speed of {} kilometers per hour", lambda x: x / 10),
("{} percent complete", lambda x: x / 100),
("Scored {} points in the game", lambda x: x / 10),
("The distance is {} meters", lambda x: x),
]
data = []
for _ in range(n_samples):
template, transform = templates[np.random.randint(len(templates))]
value = np.random.uniform(0, 100)
text = template.format(round(value, 1))
target = transform(value)
data.append((text, target))
return dataWe create a synthetic dataset that pairs natural language sentences with corresponding numerical values. By using varied templates such as temperatures, ratings, and percentages, we ensure the model learns diverse text–number relationships. This controlled setup helps us simulate realistic regression tasks without relying on external data. Check out the FULL CODES here.
class SimpleTokenizer:
def __init__(self):
self.word2idx = {"<PAD>": 0, "<UNK>": 1}
self.idx2word = {0: "<PAD>", 1: "<UNK>"}
self.vocab_size = 2
def fit(self, texts):
"""Build vocabulary from texts"""
words = []
for text in texts:
words.extend(re.findall(r'\w+|[^\w\s]', text.lower()))
word_counts = Counter(words)
for word, _ in word_counts.most_common():
if word not in self.word2idx:
self.word2idx[word] = self.vocab_size
self.idx2word[self.vocab_size] = word
self.vocab_size += 1
def encode(self, text, max_len=20):
"""Convert text to token indices"""
words = re.findall(r'\w+|[^\w\s]', text.lower())
indices = [self.word2idx.get(w, 1) for w in words]
if len(indices) < max_len:
indices += [0] * (max_len - len(indices))
else:
indices = indices[:max_len]
return indicesWe design a simple tokenizer to convert raw text into numerical tokens that the model can process. It builds a vocabulary from all unique words and maps each to an index, handling unknown words and padding automatically. This step ensures our textual inputs are transformed into consistent, machine-readable sequences for training. Check out the FULL CODES here.
class RLMDataset(Dataset):
def __init__(self, data, tokenizer, max_len=20):
self.data = data
self.tokenizer = tokenizer
self.max_len = max_len
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
text, target = self.data[idx]
tokens = self.tokenizer.encode(text, self.max_len)
return torch.tensor(tokens), torch.tensor([target], dtype=torch.float32)
class RegressionLanguageModel(nn.Module):
def __init__(self, vocab_size, embed_dim=128, num_heads=4, num_layers=2,
dropout=0.1, max_len=20):
super().__init__()
self.token_embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
self.position_embedding = nn.Embedding(max_len, embed_dim)
encoder_layer = nn.TransformerEncoderLayer(
d_model=embed_dim,
nhead=num_heads,
dim_feedforward=embed_dim * 4,
dropout=dropout,
batch_first=True
)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
self.fc1 = nn.Linear(embed_dim, 64)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(dropout)
self.fc2 = nn.Linear(64, 1)
self.max_len = max_len
def forward(self, x):
batch_size, seq_len = x.shape
positions = torch.arange(0, seq_len, device=x.device).unsqueeze(0).expand(batch_size, -1)
token_embed = self.token_embedding(x)
pos_embed = self.position_embedding(positions)
embeddings = token_embed + pos_embed
padding_mask = (x == 0)
encoded = self.transformer(embeddings, src_key_padding_mask=padding_mask)
mask_expanded = (~padding_mask).unsqueeze(-1).float()
summed = (encoded * mask_expanded).sum(dim=1)
pooled = summed / mask_expanded.sum(dim=1)
x = self.fc1(pooled)
x = self.relu(x)
x = self.dropout(x)
output = self.fc2(x)
return outputWe package our text–number pairs into a PyTorch Dataset, where we tokenize each sentence and return tensors ready for batching. We then build a Transformer-based RLM: token and positional embeddings flow through a multi-layer encoder, we mean-pool non-padded tokens, and feed the result to a small MLP head for regression. In effect, we allow the encoder to learn numerical cues from language, while the head maps them to a single continuous value. Check out the FULL CODES here.
def train_rlm(model, train_loader, val_loader, epochs=15, lr=0.001):
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=lr)
train_losses, val_losses = [], []
print(f"\n📊 Training on {device}")
print("-" * 60)
for epoch in range(epochs):
model.train()
train_loss = 0
for tokens, targets in train_loader:
tokens, targets = tokens.to(device), targets.to(device)
optimizer.zero_grad()
outputs = model(tokens)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
train_loss /= len(train_loader)
train_losses.append(train_loss)
model.eval()
val_loss = 0
with torch.no_grad():
for tokens, targets in val_loader:
tokens, targets = tokens.to(device), targets.to(device)
outputs = model(tokens)
loss = criterion(outputs, targets)
val_loss += loss.item()
val_loss /= len(val_loader)
val_losses.append(val_loss)
print(f"Epoch {epoch+1:2d}/{epochs} | Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f}")
return train_losses, val_lossesWe train the model using Adam and MSE loss on a GPU, if available, iterating over mini-batches to backpropagate and update weights. We switch to evaluation mode for validation at the end of each epoch, track training and validation losses, and print progress so we can see the learning dynamics in real-time. Check out the FULL CODES here.
print("\n📝 Generating synthetic data...")
data = generate_synthetic_data(2000)
split_idx = int(0.8 * len(data))
train_data, val_data = data[:split_idx], data[split_idx:]
print(f"Train samples: {len(train_data)}, Val samples: {len(val_data)}")
print("\n🔤 Building tokenizer...")
tokenizer = SimpleTokenizer()
tokenizer.fit([text for text, _ in train_data])
print(f"Vocabulary size: {tokenizer.vocab_size}")
train_dataset = RLMDataset(train_data, tokenizer)
val_dataset = RLMDataset(val_data, tokenizer)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32)
print("\n🏗️ Building Regression Language Model...")
model = RegressionLanguageModel(vocab_size=tokenizer.vocab_size)
print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}")
train_losses, val_losses = train_rlm(model, train_loader, val_loader)
plt.figure(figsize=(10, 4))
plt.plot(train_losses, label="Train Loss", linewidth=2)
plt.plot(val_losses, label="Val Loss", linewidth=2)
plt.xlabel('Epoch')
plt.ylabel('Loss (MSE)')
plt.title('RLM Training Progress')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()
print("\n🎯 Testing Predictions:")
print("-" * 60)
test_examples = [
"The temperature is 25.5 degrees",
"I rate this 8.0 out of ten",
"The price is 45.0 dollars",
"75.0 percent complete"
]
with torch.no_grad():
for text in test_examples:
tokens = torch.tensor([tokenizer.encode(text)]).to(device)
prediction = model(tokens).item()
print(f"Input: {text}")
print(f"Predicted value: {prediction:.4f}\n")
print("✅ RLM Tutorial Complete!")We generate and split synthetic data, fit our tokenizer, wrap everything in PyTorch datasets/loaders, and build the Transformer-based RLM. We train the model, visualize loss curves to verify learning, and then run a few natural-language test prompts to see the predicted continuous values. With that, we complete the end-to-end RLM pipeline.
In conclusion, we successfully designed, trained, and evaluated a Regression Language Model capable of predicting continuous values from textual inputs. We observe how combining positional embeddings, transformer encoders, and a simple regression head enables the model to capture the numerical semantics embedded in language. By generating synthetic data, visualizing training progress, and testing predictions, we demonstrate how RLMs bridge the gap between language understanding and numerical reasoning.
Check out the FULL CODES here. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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