CosineSimilarityHeatmap

Generates an interactive heatmap to visualize the cosine similarities among embeddings derived from a given model.

Purpose: This function is designed to visually analyze the cosine similarities of embeddings from a specific model. Cosine similarity, a measure of the cosine of the angle between two vectors, aids in understanding the orientation and similarity of vectors in multi-dimensional space. This is particularly valuable for exploring text embeddings and their relative similarities among documents, words, or phrases.

Test Mechanism: The function operates through a sequence of steps to visualize cosine similarities. Initially, embeddings are extracted for each dataset entry using the designated model. Following this, the function computes the pairwise cosine similarities among these embeddings. The computed similarities are then displayed in an interactive heatmap.

Signs of High Risk: - High similarity values (close to 1) across the heatmap might not always be indicative of a risk; however, in contexts where diverse perspectives or features are desired, this could suggest a lack of diversity in the model’s learning process or potential redundancy. - Similarly, low similarity values (close to -1) indicate strong dissimilarity, which could be beneficial in scenarios demanding diverse outputs. However, in cases where consistency is needed, these low values might highlight that the model is unable to capture a coherent set of features from the data, potentially leading to poor performance on related tasks.

Strengths: - Provides an interactive and intuitive visual representation of embedding similarities, facilitating easy exploration and analysis. - Allows customization of visual elements such as title, axis labels, and color scale to suit specific analytical needs and preferences.

Limitations: - As the number of embeddings increases, the effectiveness of the heatmap might diminish due to overcrowding, making it hard to discern detailed similarities. - The interpretation of the heatmap heavily relies on the appropriate setting of the color scale, as incorrect settings can lead to misleading visual interpretations.