PrincetonML/SIF

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1085 stars · 303 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python research library implementing the Smooth Inverse Frequency (SIF) weighting scheme for computing sentence embeddings, from the ICLR 2017 paper 'A Simple but Tough-to-Beat Baseline for Sentence Embeddings'. It includes scripts for generating embeddings, evaluating textual similarity, and training on supervised tasks.

Use cases

• compute sentence embeddings with SIF weighting
• evaluate textual similarity between sentences
• reproduce results from the SIF paper
• build a simple baseline for sentence representation tasks
• train models on sentence similarity and entailment tasks

When to choose

• you need a simple, strong baseline for sentence embeddings
• you want to reproduce or build on the SIF research
• you're working with GloVe word embeddings and need sentence-level vectors

When to avoid

• you need modern transformer-based sentence embeddings like Sentence-BERT
• you want a production-ready, actively maintained library
• you need support for recent deep learning frameworks instead of Theano/Lasagne

Facets

library · maturity maintenance

nlp machine-learning machine-learning python sentence-embeddings smooth-inverse-frequency textual-similarity research-code word-embeddings natural-language-processing research

Member repositories

For agents

markdown · JSON · MCP: product_card(name="PrincetonML/SIF")