v1.2.0 — Text Embedder & Contrastive Training

Text Embedder & Contrastive Training. A mean-pooled bi-encoder embedding model
(TextEmbedder) and the InfoNCE objective to train it (in-batch and optional hard
negatives). Additive and backward-compatible.

Added

• TextEmbedder (polaris.models): a bi-encoder tower that emits a single
  (optionally L2-normalized) embedding per text — the shared TransformerEncoder
  trunk, mask-aware mean pooling, an optional linear projection, and optional
  normalization. forward(Batch) and encode(input_ids, attention_mask).
• mean_pool (polaris.models): the mask-aware mean-pool, factored out of the two
  classifiers (which now reuse it) so the embedder can share it.
• HasEncoder (polaris.models): a structural protocol for models that wrap a
  TransformerEncoder as encoder, used to type weight transfer.
• ContrastiveBatch / collate_contrastive (polaris.collation): collate
  (anchor, positive) or (anchor, positive, hard_negatives) pairs into aligned
  batches (hard negatives flattened with a per-anchor count).
• info_nce_loss (polaris.training.losses): InfoNCE contrastive loss with
  in-batch negatives, optional per-anchor hard negatives, and an optional symmetric
  (bidirectional) term.
• train_contrastive (polaris.training): a minimal seeded driver that trains a
  TextEmbedder with info_nce_loss.
• The build_model factory now recognizes the "embedder" model type.

Changed

• MaskedLanguageModel.transfer_encoder_to accepts any HasEncoder (the
  classifier or the new TextEmbedder) rather than only the classifier — a widened
  parameter, backward-compatible.