Train a Text Classifier
Embed, pool, and predict sentiment.
Train a Text Classifier is a free Deep Learning Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Deep Learning Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
The Goal: Predict a Label
A text classifier reads a sentence and predicts a label, like positive or negative sentiment for a movie review. 🎬
Step One: Encode the Text
Reuse your pipeline: tokenize each review and map tokens to ids, turning every sentence into a list of integers.
Step Two: Embed the Ids
Feed those ids into an embedding layer. Each review becomes a sequence of dense word vectors the model can process.
self.emb = nn.Embedding(vocab_size, 32)Step Three: Pool the Vectors
A sentence has many vectors but you need one. Pooling, often a mean over the words, collapses them into a single vector.
pooled = vecs.mean(dim=1) # average over the sequenceStep Four: A Linear Head
Send the pooled vector through a linear layer to produce one score per class. These raw scores are called logits.
self.fc = nn.Linear(32, num_classes)Assemble the Model
Stack embed, pool, and the linear head in a forward method. That is a complete, tiny text classifier.
def forward(self, ids):
x = self.emb(ids).mean(dim=1)
return self.fc(x)Pick the Loss
For multiclass labels use cross-entropy loss. It expects raw logits and the integer class index as the target.
loss_fn = nn.CrossEntropyLoss()Choose an Optimizer
An optimizer like Adam updates the embedding and linear weights together as it minimizes the loss.
opt = torch.optim.Adam(model.parameters(), lr=1e-3)The Training Loop
For each batch: forward, compute loss, backward, and step. Repeat over the data for several epochs.
logits = model(ids)
loss = loss_fn(logits, labels)
loss.backward()
opt.step()Make a Prediction
At inference, take the class with the highest logit using argmax to get the predicted label for new text.
pred = model(ids).argmax(dim=1)Measure Accuracy
Compare predictions to true labels to compute accuracy. Watch it climb as the embedding learns sentiment patterns.
Quick Check
In this classifier, what turns a sequence of word vectors into one vector?
Recap
You embed ids, pool them into one vector, classify with a linear head, and train with cross-entropy to label text. ✅
Frequently asked questions
Is the “Train a Text Classifier” lesson free?
Yes — the full text of “Train a Text Classifier” is free to read here on the web, and the Deep Learning Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Deep Learning Academy course, upgrade to CoddyKit PRO.
What will I learn in “Train a Text Classifier”?
Embed, pool, and predict sentiment. You practise Deep Learning Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Deep Learning Academy?
No prior experience is required. Deep Learning Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Train a Text Classifier” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Deep Learning Academy lesson?
Yes. Every Deep Learning Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.