embeddings-evaluator 1.0.0

Tool for analyzing and comparing embedding models through pairwise cosine similarity distributions

Embeddings Evaluator

A Python package for analyzing and comparing embedding models through pairwise cosine similarity distributions.

Features

• Pairwise cosine similarity distribution analysis
• Statistical measures:
  • Mean (μ)
  • Standard deviation (σ)
  • Median (m)
  • Peak location and amplitude
• Multi-model comparison visualization

Installation

pip install -r requirements.txt

Usage

import numpy as np
from embeddings_evaluator import plot_model_comparison
from embeddings_evaluator.comparison import save_comparison_plot

# Load your embeddings into a dictionary
embeddings_dict = {
    "Model A": embeddings_a,  # numpy array of shape (n_docs, embedding_dim)
    "Model B": embeddings_b
}

# Generate comparison plot
fig = plot_model_comparison(embeddings_dict)
save_comparison_plot(fig, 'comparison.png')

Example with Faiss Indices

import faiss
import numpy as np
from embeddings_evaluator import plot_model_comparison

# Load embeddings from faiss indices
def load_faiss_embeddings(index_path):
    index = faiss.read_index(index_path)
    if isinstance(index, faiss.IndexFlatL2):
        num_vectors = index.ntotal
        dimension = index.d
        embeddings = np.zeros((num_vectors, dimension), dtype=np.float32)
        for i in range(num_vectors):
            embeddings[i] = index.reconstruct(i)
        return embeddings
    raise ValueError("Unsupported index type")

# Load multiple models
embeddings_dict = {}
for size in [250, 500, 1000, 2000, 4000]:
    embeddings = load_faiss_embeddings(f"faiss_embeddings/{size}/index.faiss")
    # Normalize for cosine similarity
    embeddings = embeddings / np.linalg.norm(embeddings, axis=1)[:, np.newaxis]
    embeddings_dict[f"Model {size}"] = embeddings

# Generate visualization
fig = plot_model_comparison(embeddings_dict)
save_comparison_plot(fig, 'model_comparison.png')

Output

The tool provides:

1. Statistical Measures for each model:

• Mean cosine similarity (μ)
• Standard deviation (σ)
• Median (m)
• Peak location and amplitude

2. Visualization:

• Overlaid probability density histograms
• Statistical annotations
• Peak coordinates
• Vertical lines at mean values
• [0,1] bounded cosine similarity range

Requirements

• numpy
• pandas
• plotly
• scipy
• faiss-cpu (for faiss index support)