If I am creating one hot embedding vector for say food, let’s say those have keys, pizza, burger, sushi and drink If I am encoding an order that has burger and drink, my encoding vector will be 0 1 0 1?

Yes, that’s correct.

Given the keys in this order: [pizza, burger, sushi, drink]

An order with burger and drink would be encoded as: [0, 1, 0, 1] → 0 for pizza, 1 for burger, 0 for sushi, 1 for drink.

Each index corresponds to whether that item is present (1) or not (0) in the order.

No, this is not strictly one-hot encoding — it’s actually called a multi-hot encoding (or multi-label binary encoding).

Difference:

  • One-hot encoding: Used when there’s only one possible category per instance. Example: If an order could be only one of pizza, burger, sushi, or drink, then:

    • burger → [0, 1, 0, 0]
    • sushi → [0, 0, 1, 0]

  • Multi-hot encoding: Used when multiple categories can apply simultaneously, like in your case with an order containing both burger and drink.

    • burger + drink → [0, 1, 0, 1]

So your example is multi-hot encoding, not one-hot.

When we should use one hot encoding and when we can use multi hot, in context of tokenization

Great question — especially in the context of tokenization and NLP/ML pipelines, the choice between one-hot and multi-hot encoding depends on the nature of your data and task:

✅ Use One-Hot Encoding When:

  • You have a single label/token per instance (e.g., classification tasks).
  • Your vocabulary is mutually exclusive — each input can only map to one category at a time.
  • You’re encoding individual tokens in NLP — common in early NLP models or toy examples.

📌 Example use cases:

  • Classifying a sentence sentiment: [positive, neutral, negative]
  • Encoding tokens in a sequence for RNNs: word = "pizza" → [0, 0, 1, 0, ..., 0]

✅ Use Multi-Hot Encoding When:

  • You have multiple possible labels per instance.
  • You need to represent sets of tokens or features — not just individual ones.
  • You’re encoding entire documents or inputs as a presence/absence vector over a vocabulary.

📌 Example use cases:

  • Text classification where documents can belong to multiple categories.
  • Representing a bag-of-words or bag-of-items (like your food order).
  • Building inputs for simple neural nets without sequence models.

⚠️ In Tokenization:

Most modern tokenizers (like BPE or WordPiece) don’t use one-hot or multi-hot encodings directly. Instead:

  • Tokens are mapped to integers (IDs).
  • These IDs are then passed through embedding layers, which learn dense vector representations (instead of sparse one-hot vectors).

So:

  • One-hot/multi-hot are mostly used in simpler, classical ML models.
  • For modern NLP, use token IDs + embedding layers instead.