Embeddings pooling

Pooling — Lodestar.Embeddings.Pooling

A transformer returns one vector per token. A sentence embedding is one vector. Pooling is the step between, and getting it wrong is the quiet way to make every downstream similarity slightly untrue.

Lodestar.Embeddings.Pooling holds one static class, Pooler, implementing the recipe sentence-transformers uses: masked mean, then L2 normalization.

Which call?

You have	You want	Call
one sequence	a sentence embedding	MeanPoolAndNormalize
a padded batch	one embedding per sequence	MeanPoolAndNormalizeBatch
one sequence	the mean only, unnormalized	MeanPool
a padded batch	the means only	MeanPoolBatch
a vector already pooled	it scaled to unit length	L2Normalize

The two AndNormalize forms are the ones to reach for. The others exist because a caller who pools now and normalizes later, or who pools something this package did not produce, should not have to reimplement half the recipe.

The mask is the whole point

A batch is padded to its longest sequence, and the padding positions carry embeddings — the model computed something for them. Averaging those in would drag every short sequence's vector toward whatever the padding token happens to mean.

The mask is what excludes them, and every call here takes one. The mean divides by the number of real tokens, not by seqLen: sum(embeddings × mask) / max(sum(mask), 1e-9), which is sentence-transformers' own formula down to the clamp that keeps an all-padding sequence from dividing by zero.

Why normalize

Cosine similarity is a dot product only on unit vectors, which is what EmbeddingIndex relies on. Normalizing at pooling time means every consumer downstream gets that for free.

The same bits on both target frameworks

This namespace makes the opposite trade from VectorMath. In L2Normalize the sum of squares is accumulated in double and deliberately not vectorized: a Vector<float> accumulator would lose precision and make the result depend on the SIMD width of the machine that ran it. The scaling pass, which is exact whichever way it is done, is vectorized.

The result is a pooled vector that is bit-identical between net10.0 and netstandard2.0, and between machines of different vector widths.

Types

Type	What it is
Pooler	The five pooling calls.

See also

• Embeddings, end to end — where pooling sits in the chain.
• Python → C# equivalence — the pooling rows.