--- name: embedding-analysis-146a53702970 description: Compute and analyze embeddings for dataset quality, distribution comparison, semantic deduplication, diversity measurement, and similarity-based filtering. Covers sentence-transformers, embedding space diagnostics, and 2025-2026 literature techniques (NeMo Curator semantic dedup, embedding similarity metrics for data selection, DataRater-style quality scoring). tags: [embeddings, semantic-dedup, distribution-analysis, sentence-transformers, data-quality, metric-learning, dataset-curation] --- # Embedding Analysis for Dataset Curation ## Overview Embeddings turn unstructured text/images into a vector space where quality, diversity, and redundancy become measurable. This skill covers embedding-based dataset curation using sentence-transformers, distribution comparison metrics, semantic deduplication, and quality scoring techniques from the 2025-2026 literature. ## When to Use Use this skill when: - Detecting near-duplicate or semantically redundant examples. - Comparing embedding distributions across dataset splits (train vs. val vs. test). - Measuring dataset diversity or coverage in embedding space. - Filtering examples based on embedding distance to a "gold" reference set. - Applying NeMo Curator-style semantic dedup at scale. - Computing DataRater-style quality signals from embedding neighborhoods. Do not use for: - Raw text deduplication (exact match) — use simple hashing. - Model training — use `transformers` or `trl` skills. - Vector database persistence — use `chromadb`, `milvus`, or `qdrant` skills. ## Installation ```bash uv pip install sentence-transformers umap-learn scikit-learn numpy ``` ## Core Workflow ### 1. Generate Embeddings ```python from sentence_transformers import SentenceTransformer import numpy as np # Choose model based on domain model = SentenceTransformer("all-MiniLM-L6-v2") # fast, general # model = SentenceTransformer("all-mpnet-base-v2") # higher quality # model = SentenceTransformer("BAAI/bge-large-en-v1.5") # retrieval-optimized # model = SentenceTransformer("intfloat/multilingual-e5-large") # multilingual # Batched encoding (memory-efficient) embeddings = model.encode( texts, batch_size=64, show_progress_bar=True, normalize_embeddings=True, # cosine similarity via dot product convert_to_numpy=True, ) ``` ### 2. Semantic Deduplication Semantic dedup removes examples that are meaning-equivalent but not text-identical. From NeMo Curator (NVIDIA, 2024-2025) and academic literature. #### Clustering-Based Approach (NeMo Curator SemDedup) ```python from sklearn.cluster import MiniBatchKMeans from sklearn.metrics.pairwise import cosine_similarity def semantic_dedup(embeddings, threshold=0.95, n_clusters=100): """ Cluster embeddings, then remove near-duplicates within each cluster. threshold: cosine similarity above which two examples are duplicates. Returns: boolean mask (True = keep, False = duplicate). """ n = len(embeddings) keep = np.ones(n, dtype=bool) # Coarse clustering for efficiency (O(n * k) instead of O(n^2)) k = min(n_clusters, n // 10) clusters = MiniBatchKMeans(n_clusters=k, random_state=42, batch_size=1024).fit_predict(embeddings) for c in range(k): idx = np.where(clusters == c)[0] if len(idx) < 2: continue sim = cosine_similarity(embeddings[idx]) # Mark duplicates (lower-index kept, higher-index removed) for i in range(len(idx)): if not keep[idx[i]]: continue dupes = np.where(sim[i, i+1:] > threshold)[0] for d in dupes: keep[idx[i + 1 + d]] = False return keep ``` #### Connected Components Approach (LSHBloom, 2025) ```python # For internet-scale dedup (>100M examples): # Use LSH for approximate nearest neighbor + union-find from sklearn.neighbors import NearestNeighbors def lsh_semantic_dedup(embeddings, threshold=0.90, n_neighbors=10): """Finds clusters of semantically similar examples via NN graph.""" nn = NearestNeighbors(n_neighbors=n_neighbors, metric="cosine", n_jobs=-1) nn.fit(embeddings) distances, indices = nn.kneighbors(embeddings) # Union-find to merge connected components parent = np.arange(len(embeddings)) def find(x): while parent[x] != x: parent[x] = parent[parent[x]] x = parent[x] return x def union(a, b): parent[find(a)] = find(b) for i in range(len(embeddings)): for j, d in zip(indices[i], distances[i]): if i != j and (1 - d) > threshold: # cosine dist → similarity union(i, j) # Keep one per component components = {} for i in range(len(embeddings)): root = find(i) if root not in components: components[root] = i return list(components.values()) # indices to keep ``` ### 3. Distribution Comparison Across Splits Compare embedding distributions between train/val/test to detect drift or bias. ```python from scipy.spatial.distance import jensenshannon from scipy.stats import wasserstein_distance def embedding_distribution_shift(train_emb, test_emb, n_bins=50): """Quantify shift between train and test embedding distributions.""" # Project to 1D for distribution comparison from sklearn.decomposition import PCA pca = PCA(n_components=1, random_state=42).fit( np.concatenate([train_emb, test_emb]) ) train_1d = pca.transform(train_emb).ravel() test_1d = pca.transform(test_emb).ravel() # Histogram-based metrics hist_range = (min(train_1d.min(), test_1d.min()), max(train_1d.max(), test_1d.max())) train_hist, _ = np.histogram(train_1d, bins=n_bins, range=hist_range, density=True) test_hist, _ = np.histogram(test_1d, bins=n_bins, range=hist_range, density=True) # Jensen-Shannon divergence (0 = identical, 1 = maximally different) js_div = jensenshannon(train_hist + 1e-10, test_hist + 1e-10) # Wasserstein (Earth Mover's) distance w_dist = wasserstein_distance(train_1d, test_1d) return {"js_divergence": js_div, "wasserstein": w_dist} def per_dimension_shift(train_emb, test_emb): """Per-dimension mean shift — identifies which semantic axes drifted.""" train_mean = train_emb.mean(axis=0) test_mean = test_emb.mean(axis=0) cos_sim = np.dot(train_mean, test_mean) / ( np.linalg.norm(train_mean) * np.linalg.norm(test_mean) ) max_dim_shift = np.argmax(np.abs(train_mean - test_mean)) return {"mean_cosine": cos_sim, "max_shift_dim": int(max_dim_shift)} ``` ### 4. Embedding Quality Scoring (DataRater-Inspired, 2025) DataRater (Calian et al., NeurIPS 2025) meta-learns a quality function over embedding neighborhoods. Below is a practical approximation. ```python def embedding_quality_score(embeddings, k=20): """ Score each example by embedding neighborhood coherence. Low coherence → outlier / potential noise. High coherence → in-distribution, likely high quality. """ nn = NearestNeighbors(n_neighbors=k + 1, metric="cosine") nn.fit(embeddings) distances, _ = nn.kneighbors(embeddings) # Exclude self-distance (index 0) mean_dist = distances[:, 1:].mean(axis=1) # Normalize to [0, 1] — lower distance = higher quality scores = 1 - (mean_dist - mean_dist.min()) / (mean_dist.max() - mean_dist.min() + 1e-10) return scores ``` ### 5. Diversity Measurement ```python def embedding_diversity(embeddings, n_clusters=50): """ Measure dataset diversity via cluster coverage. Returns: coverage ratio and entropy of cluster assignments. """ k = min(n_clusters, len(embeddings) // 10) clusters = MiniBatchKMeans(n_clusters=k, random_state=42, batch_size=1024).fit_predict(embeddings) # Cluster coverage: fraction of clusters that are non-empty unique, counts = np.unique(clusters, return_counts=True) coverage = len(unique) / k # Entropy: higher = more uniform distribution across clusters probs = counts / counts.sum() entropy = -np.sum(probs * np.log(probs + 1e-10)) / np.log(k) # normalized return {"coverage": coverage, "normalized_entropy": entropy} def embedding_redundancy_score(embeddings, sample_size=5000): """ Estimate redundancy: average pairwise cosine similarity. High score → dataset is homogeneous; needs diversification. """ idx = np.random.choice(len(embeddings), min(sample_size, len(embeddings)), replace=False) sample = embeddings[idx] sim = cosine_similarity(sample) # Exclude diagonal mask = ~np.eye(len(sample), dtype=bool) return float(sim[mask].mean()) ``` ## Perplexity-Based Filtering (GRAPE Score, 2025) Use a small reference model to score data quality by perplexity. High perplexity = unfamiliar/hard/noisy. ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch def grape_score(texts, model_name="gpt2", batch_size=8): """ GRAPE-style perplexity scoring. Lower perplexity → more in-distribution, likely higher quality. Very high perplexity → gibberish, noise, or out-of-domain. """ tokenizer = AutoTokenizer.from_pretrained(model_name) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token model = AutoModelForCausalLM.from_pretrained(model_name).eval() scores = [] for i in range(0, len(texts), batch_size): batch = texts[i:i + batch_size] enc = tokenizer(batch, return_tensors="pt", padding=True, truncation=True, max_length=512) with torch.no_grad(): outputs = model(**enc, labels=enc["input_ids"]) loss = outputs.loss # average NLL across batch ppl = torch.exp(loss).item() scores.append(ppl) return np.array(scores) ``` ## Quality Gate An embedding-based curation pass is complete when: - Semantic dedup threshold is justified and documented. - Train/val/test embedding distributions are compared (JS divergence, Wasserstein). - Diversity and redundancy metrics are computed before and after curation. - Outlier scores are reviewed — low neighborhood coherence may indicate noise or rare-but-valuable data. - Results are reproducible (fixed seeds, saved model versions). --- Source attribution: mkurman Original project: https://github.com/mkurman/zorai/blob/d06ee7597049567dab92db22d41b2764cf89094a/skills/data-lattice/embedding-analysis/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 zorai contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: mkurman Original project: https://github.com/mkurman/zorai/blob/d06ee7597049567dab92db22d41b2764cf89094a/skills/data-lattice/embedding-analysis/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Product Management Roles: Data Analyst, Data Scientist, Market Research Analyst, ML / AI Engineer, Product Manager Specialties: API, Testing Tools: Numpy, Scikit Learn, Sentence Transformers, Umap Learn Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: c9c3a11404263d3b9c5dadf427da3945496b21861773f54fb8b79ec8a4cf368a