Building NLP Models with TensorFlow

So far in this module, you have learned how text is processed, vectorized, and used in Machine Learning and Deep Learning models. You also saw how advanced tasks like NER work conceptually.

In this lesson, we bring everything together and learn how to actually build an NLP model using TensorFlow.

This lesson focuses on understanding the full pipeline, not just writing code blindly. After this lesson, you will clearly understand how real NLP models are built.

What Does “Building an NLP Model” Mean?

Building an NLP model means creating a system that can:

• Take raw text as input
• Convert text into numbers
• Learn patterns from data
• Make predictions on new text

TensorFlow helps us build and train such models efficiently.

Typical NLP Model Pipeline (End-to-End)

Almost every NLP model follows this pipeline:

1. Text collection
2. Text preprocessing
3. Tokenization
4. Vectorization / Embedding
5. Neural network modeling
6. Training and evaluation

TensorFlow provides tools for each of these steps.

Where to Run and Practice This Code

Recommended environments:

• Google Colab (best for beginners)
• Jupyter Notebook with TensorFlow installed

Google Colab is preferred because:

• No installation required
• Free GPU support
• Easy experimentation

Example Problem: Text Classification

We will build a simple NLP model that classifies text into categories.

Task:

• Input: sentence
• Output: class label (0 or 1)

This is a foundational NLP task used in:

• Spam detection
• Sentiment analysis
• Topic classification

Step 1: Preparing the Dataset

We start with a small text dataset and labels.

texts = [
    "I love this product",
    "This is a terrible experience",
    "Amazing service and quality",
    "I hate this item",
    "Very satisfied with the purchase",
    "Worst product ever"
]

labels = [1, 0, 1, 0, 1, 0]

Here:

• 1 = positive sentiment
• 0 = negative sentiment

Step 2: Tokenization and Vectorization

Neural networks cannot read raw text. We convert words into integer sequences using a tokenizer.

from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

tokenizer = Tokenizer(num_words=1000)
tokenizer.fit_on_texts(texts)

sequences = tokenizer.texts_to_sequences(texts)
padded_sequences = pad_sequences(sequences, maxlen=6)

What happens here:

• Each word gets a unique number
• Sentences become number sequences
• Padding ensures equal length

Step 3: Building the NLP Model

Now we define a neural network using TensorFlow (Keras API).

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense

model = Sequential([
    Embedding(input_dim=1000, output_dim=16, input_length=6),
    LSTM(32),
    Dense(1, activation='sigmoid')
])

model.compile(
    optimizer='adam',
    loss='binary_crossentropy',
    metrics=['accuracy']
)

This model:

• Learns word embeddings automatically
• Uses LSTM to capture sequence patterns
• Outputs a probability score

Step 4: Training the Model

We now train the model on our data.

model.fit(
    padded_sequences,
    labels,
    epochs=10,
    verbose=1
)

During training, the model learns how words and sequences relate to labels.

Step 5: Making Predictions

Once trained, the model can predict sentiment for new sentences.

test_text = ["I really love this service"]
test_seq = tokenizer.texts_to_sequences(test_text)
test_pad = pad_sequences(test_seq, maxlen=6)

prediction = model.predict(test_pad)
print(prediction)

If the output is closer to 1 → positive If closer to 0 → negative

How This Connects to Real NLP Systems

This same structure is used in:

• Spam classifiers
• Sentiment analysis tools
• Customer feedback analysis
• Chatbot intent detection

Larger systems use more data and deeper models, but the core pipeline remains the same.

Homework / Assignment

Practical:

• Add more sentences to the dataset
• Increase vocabulary size
• Experiment with GRU instead of LSTM

Theory:

• Explain the role of the Embedding layer
• Why padding is necessary

Practice Questions

Quick Quiz

Quick Recap

• NLP models follow a clear pipeline
• TensorFlow simplifies model building
• Tokenization and embeddings are essential
• LSTM captures sequence patterns
• This foundation applies to advanced NLP systems

Next lesson: Transformers – Introduction