EmbeddingsVisualization2D

Visualizes 2D representation of text embeddings generated by a model using t-SNE technique.

Purpose

The objective of this metric is to provide a visual 2D representation of the embeddings created by a text embedding machine learning model. By doing so, it aids in analyzing the embedding space created by the model and helps in understanding how the learned embeddings are distributed and how they relate to each other.

Test Mechanism

This metric uses the t-Distributed Stochastic Neighbor Embedding (t-SNE) technique, which is a tool for visualizing high-dimensional data by reducing the dimensionality to 2. The perplexity parameter for t-SNE is set to the value provided by the user. If the input perplexity value is greater than the number of samples, the perplexity is adjusted to be one less than the number of samples. Following the reduction of dimensionality, a scatter plot is produced depicting each embedding as a data point in the visualized 2D plane.

Signs of High Risk

  • If the embeddings are highly concentrated in a specific region of the plane, it might indicate that the model is not learning diverse representations of the text.
  • Wide gaps or partitions in the visualization could suggest that the model is over-segmenting in the embedding space and may lead to poor generalization.

Strengths

  • Offers a powerful visual tool that can assist in understanding and interpreting high-dimensional embeddings, which could otherwise be difficult to visualize.
  • It is model-agnostic and can be used with any machine learning model that produces text embeddings.
  • t-SNE visualization helps in focusing on local structures and preserves the proximity of points that are close together in the original high-dimensional space.

Limitations

  • The reduction of high-dimensional data to 2D can result in loss of some information, which may lead to misinterpretation.
  • Due to its stochastic nature, t-SNE can produce different results when run multiple times with the same parameters, leading to potential inconsistency in interpretation.
  • It is designed for visual exploration and not for downstream tasks; that is, the 2D embeddings generated should not be directly used for further training or analysis.