core/nn/sentence/sentence_encoder library
Sentence-Transformers style embedder.
A SentenceEncoder wraps a TextTransformer backbone with a
pooling step (mean over tokens or the first "CLS"-style token)
and an optional row-wise L2 normalization, producing a single
[1, embedDim] sentence embedding per input.
Matches the recipe from
github.com/huggingface/sentence-transformers: backbone
output [seqLen, D] → pool → optional normalize → dense
embedding suitable for cosine-similarity search and reranking.
Classes
- SentenceEncoder
- TokenEncoder
-
Anything that maps
[seqLen]token indices to a[seqLen, embedDim]feature matrix and reports its output width. Both TextTransformer and the BERT-styleBertModelimplement this so SentenceEncoder and CrossEncoder can wrap either backbone.
Enums
Functions
-
l2NormalizeRow(
Tensor x, {double eps = 1e-12}) → Tensor -
Row-wise L2 normalize a
[1, D]tensor. -
poolTokens(
Tensor tokenFeatures, PoolingMode mode) → Tensor -
Pool a token feature matrix
[seqLen, embedDim]to a sentence vector[1, embedDim].