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.