Concept metrics#

We can quantify the quality of a concept using an F1 score from the labeled examples.

Moreover, the choice of an embedding will affect the quality of the concept in practice. Some embeddings can capture nuanced concepts much better than other embeddings, especially when the model is much larger. See Embeddings for details on embeddings.

The quality of a concept with an embedding is determined by computing the F1 score using at most 15 cross-validation splits. A single example is left out of the training set, a model is trained, and we measure the correctness of predicting the left-out example.

The human readable scores are based on this score:

  • Great: 0.95 to 1.0

  • Very good: 0.9 to 0.95

  • Good: 0.8 to 0.9

  • OK: 0.5 to 0.8

  • Not good: 0.0 to 0.5

From the UI#

From the concepts preview page, there is a metrics panel, which determines the quality for each embedding for that concept:

Toxicity is a particularly difficult concept, as it requires a deeper understanding of the meaning of the text, so larger embeddings typically do better for this concept.

From Python#

Let’s compute the metrics for our concept, with the gte-small embedding.

model_db = ll.DiskConceptModelDB(ll.DiskConceptDB())

model = model_db.get(
  'local', 'positive-product-reviews',



f1=0.9183673469387754 precision=0.9 recall=1.0 roc_auc=0.9595959595959596 overall=<OverallScore.VERY_GOOD: 'very_good'>