# `Sting.Mining`
[🔗](https://github.com/dkuku/sting/blob/v0.1.0/lib/sting/mining.ex#L1)

Embedding-level retrieval mining utilities aligned with SentenceTransformers.

The functions accept already-computed embeddings. This keeps retrieval and
mining independent of a particular encoder while preserving the selection
rules used by SentenceTransformers' utility functions.

# `community_detection`

Finds non-overlapping cosine-similarity communities in embeddings.

Communities are ordered by decreasing size, and each community starts with
its central (highest-scoring) embedding, matching SentenceTransformers.
`:threshold` defaults to `0.75`, `:min_community_size` to `10`, and
`:batch_size` to `1_024`.

# `mine_hard_negatives`

Mines hard negatives from embedding-level query, positive, and corpus inputs.

Each result is `%{query_id, positive_id, negative_id, positive_score,
negative_score}`. Positive corpus entries are excluded by default. The
selection supports SentenceTransformers' `:range_min`, `:range_max`,
`:min_score`, `:max_score`, `:absolute_margin`, `:relative_margin`, and
`:sampling_strategy` (`:top` or `:random`) options. `:query_chunk_size`
(default `100`) and `:corpus_chunk_size` (default `100_000`) bound score
blocks. Vector-equal positives are excluded on the Nx backend before each
block is ranked. `:num_negatives` defaults to `3`; `:range_min` defaults to
`0`; `:range_max` defaults to `range_min + num_negatives + 1`; and
`:sampling_strategy` defaults to `:top` (`:random` is also supported).

# `paraphrase_mining`

Finds the highest-scoring unique pairs of embeddings.

Results have the shape `[[score, first_id, second_id], ...]`, ordered by
descending score. `:query_chunk_size` and `:corpus_chunk_size` bound the
score matrix sizes; `:max_pairs` caps the returned pairs and `:top_k` limits
the candidates considered per query per corpus chunk. Defaults are `100`,
`10_000`, `500_000`, and `100` respectively; `:score_function` defaults to
`:cos_sim` and also accepts `:dot_score`, `:euclidean_sim`, `:manhattan_sim`,
or a two-argument scoring function.

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*Consult [api-reference.md](api-reference.md) for complete listing*