---

name: embeddings description: "SentenceModel, SBert, Word2Vec, EncoderType, batch embedding CLI, device selection, and multi-process encoding workflows for text2vec." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

embeddings

Use this sub-skill when you need to turn text into vectors with text2vec.

Covers

• SentenceModel / SBert inference
• EncoderType selection and sentence embedding dimension lookup
• batch encoding with the public text2vec CLI or the bundled helper script
• Word2Vec local-file and built-in Tencent lightweight model workflows
• stopwords, local cache behavior, and offline tiny fixtures
• multi-process encoding lifecycle for large SentenceModel jobs
• reference-only NGram / KenLM caveat
• short fallback notes for raw Transformers or sentence-transformers pooling

Not covered

• Pairwise similarity, dense search, or BM25 retrieval: use the sibling similarity-search sub-skill.
• Training, fine-tuning, or dataset schema work: use training-finetuning.
• Benchmark/model-choice interpretation: use evaluation-benchmarks.
• HTTP, Jina, or Gradio deployment: use serving-deployment.

Start here

• references/api-reference.md for signatures, return shapes, and supported encoder values.
• references/workflows.md for local-model, CSV/JSONL, and multi-process recipes.
• references/word2vec-and-ngram.md for Word2Vec cache/download behavior and the NGram warning.
• references/troubleshooting.md for install, download, device, and CLI failure modes.

Bundled scripts

• scripts/encode_texts.py: read one sentence per line and write JSONL or CSV embeddings.
• scripts/make_tiny_word2vec_fixture.py: generate a tiny local word2vec-format file for offline smoke tests.

Practical rule

• Use SentenceModel / SBert for general embedding generation.
• Use Word2Vec when you explicitly want word-level or cold-start lexical vectors.
• If you need scoring or search over embeddings, switch to the sibling similarity-search sub-skill.