ryankiros/skip-thoughts
Sent2Vec encoder and training code from the paper "Skip-Thought Vectors" observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 4085
- days_rel: n/a
- days_push: 2276
- n_releases_24m: 0
Adoption not part of the score
2047 stars · 531 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A Python implementation of the Skip-Thought Vectors paper, providing a sent2vec encoder that maps sentences to fixed 4800-dimensional vectors. It includes training code and pretrained model files built on Theano.
Use cases
- encode sentences into fixed-length vectors
- compute semantic similarity between sentences
- extract sentence features for classification tasks
- reproduce skip-thought vector experiments
- train sentence embedding models
When to choose
- you need to reproduce the Skip-Thought Vectors paper results
- you specifically need the original skip-thought pretrained embeddings for research
When to avoid
- you need a maintained sentence embedding library
- you cannot use Python 2.7 and Theano 0.7
- you want modern transformer-based sentence embeddings
Facets
library · maturity abandoned
machine-learning nlp machine-learning deep-learning python sentence-embeddings skip-thoughts theano sent2vec research-code embedding natural-language-processing
1 source
- readme: https://github.com/ryankiros/skip-thoughts · fetched 2026-08-28 · 154e01e1002a
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ryankiros/skip-thoughts | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="ryankiros/skip-thoughts")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem