How do I use embeddings for classification with semantic labels?

Using embeddings for classification with semantic labels usually means:

• you represent both the input and the candidate labels as vectors,
• then classify by choosing the label whose vector is most similar to the input vector.

This is especially useful when labels have meaning beyond just IDs, like:

• “refund request”
• “technical issue”
• “billing question”

instead of arbitrary classes like 0, 1, 2.

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Core idea

1. Embed the input text

   • Example: "My payment failed twice"
   • Convert it to an embedding vector.

2. Embed each label name or label description

   • "billing issue"
   • "technical issue"
   • "account access problem"

3. Compare similarities

   • Use cosine similarity or dot product.
   • Pick the label with the highest similarity.

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Basic workflow

1) Prepare semantic labels

Instead of just class IDs, use human-readable label strings.

Example:

Labels:
- "refund request"
- "login problem"
- "shipping delay"

You can also make them richer:

- "Customer wants money back because the product was unsatisfactory"
- "Customer cannot sign in or access the account"
- "Customer package has not arrived on time"

Richer descriptions often improve performance.

---

2) Create embeddings for labels

You can precompute label embeddings once.

label_texts = [
    "refund request",
    "login problem",
    "shipping delay"
]

label_embeddings = embed(label_texts)

If using semantic descriptions, embed the descriptions instead.

---

3) Embed the input

query = "I can't log into my account"
query_embedding = embed([query])

---

4) Compute similarity

scores = cosine_similarity(query_embedding, label_embeddings)
predicted_label = label_texts[argmax(scores)]

---

Example with sentence embeddings

Here’s a simple Python-style example:

from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

labels = [
    "refund request",
    "login problem",
    "shipping delay"
]

label_desc = [
    "Customer wants a refund for a purchase",
    "Customer cannot log in to the account",
    "Customer's order has not arrived yet"
]

# Precompute label embeddings
label_vecs = embed(label_desc)

# Classify a new message
text = "I'm unable to sign into my profile"
text_vec = embed([text])

scores = cosine_similarity(text_vec, label_vecs)[0]
pred_idx = np.argmax(scores)

print(labels[pred_idx])

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Two common ways to do this

1) Nearest-label matching

This is the simplest.

• Embed input
• Embed label names/descriptions
• Pick the nearest label

Good for:

• zero-shot classification
• small or medium label sets
• quickly adding new labels without retraining

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2) Train a classifier on embeddings

You can embed each text, then train a standard classifier on the vectors:

• logistic regression
• SVM
• random forest
• small neural network

Workflow:

1. Encode each training example into an embedding.
2. Train a classifier on those embeddings and labels.
3. At inference time, embed new text and predict.

Good for:

• labeled training data
• higher accuracy than pure nearest-neighbor
• stable classification over fixed classes

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Why semantic labels help

If your labels are semantically meaningful, the model can generalize better.

Example:

If the input is:

"My package still hasn't arrived"

it may match:

• "shipping delay" even if that exact phrasing never appeared in training
• because the meaning is close to the label description

This is useful for:

• few-shot classification
• zero-shot classification
• rapidly changing taxonomies

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Tips for better results

Use label descriptions, not just short names

Instead of:

• "billing"

Use:

• "billing issue: customer has a payment, invoice, or charge problem"

This gives the embedding more signal.

---

Normalize label text

Make labels consistent in style and detail.

Bad:

• "refund"
• "customer wants money back because item was damaged"
• "billing prob"

Better:

• "refund request: customer asks for money back"
• "billing issue: customer has a charge or invoice problem"
• "shipping delay: order has not arrived on time"

---

Use calibrated thresholds for “none of the above”

If you have open-set inputs, don’t always force the best label.

Example:

• if top similarity < threshold, return "unknown" or "other"

---

Try prompt formatting

For some embedding models, label text works better when formatted as a definition.

Example:

• Input: "The customer cannot reset the password"
• Label: "Password reset issue: user cannot reset or change their password"

---

Consider multi-label classification

If a text can have multiple labels, use similarity scores for all labels and return those above a threshold.

Example:

• "Refund request"
• "Shipping delay"

for a message like:

"My order is late and I want my money back"

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When to use this approach

Good choice if:

• you have semantic categories
• you want simple zero-shot or few-shot classification
• you need easy label expansion
• your data is noisy or limited

Less ideal if:

• you need very high accuracy on a fixed taxonomy
• labels are subtle and highly domain-specific
• embeddings alone miss important class boundaries

In those cases, train a supervised classifier on embeddings or fine-tune a model.

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A practical recipe

If you want a strong baseline:

1. Write a short natural-language description for each label.
2. Embed all label descriptions.
3. Embed each input.
4. Use cosine similarity.
5. Add a rejection threshold.
6. If you have enough labeled data, train a classifier on embeddings.

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Simple mental model

Think of classification as:

“Which label description is closest in meaning to this input?”

That’s the semantic-label version of embedding-based classification.

If you want, I can also show:

• a Python example using OpenAI embeddings
• a sentence-transformers example
• or a full end-to-end classification pipeline.