CosineSimilarityDistribution

Assesses the similarity between predicted text embeddings from a model using a Cosine Similarity distribution histogram.

Purpose: This metric is used to assess the degree of similarity between the embeddings produced by a text embedding model using Cosine Similarity. Cosine Similarity is a measure that calculates the cosine of the angle between two vectors. This metric is predominantly used in text analysis — in this case, to determine how closely the predicted text embeddings align with one another.

Test Mechanism: The implementation starts by computing the cosine similarity between the predicted values of the model’s test dataset. These cosine similarity scores are then plotted on a histogram with 100 bins to visualize the distribution of the scores. The x-axis of the histogram represents the computed Cosine Similarity.

Signs of High Risk:

  • If the cosine similarity scores cluster close to 1 or -1, it may indicate overfitting, as the model’s predictions are almost perfectly aligned. This could suggest that the model is not generalizable.
  • A broad spread of cosine similarity scores across the histogram may indicate a potential issue with the model’s ability to generate consistent embeddings.

Strengths:

  • Provides a visual representation of the model’s performance which is easily interpretable.
  • Can help identify patterns, trends, and outliers in the model’s alignment of predicted text embeddings.
  • Useful in measuring the similarity between vectors in multi-dimensional space, important in the case of text embeddings.

Limitations:

  • Only evaluates the similarity between outputs. It does not provide insight into the model’s ability to correctly classify or predict.
  • Cosine similarity only considers the angle between vectors and does not consider their magnitude. This can lead to high similarity scores for vectors with vastly different magnitudes but a similar direction.
  • The output is sensitive to the choice of bin number for the histogram. Different bin numbers could give a slightly altered perspective on the distribution of cosine similarity.