https://github.com/fursovia/geometric_embedding

"Zero-Training Sentence Embedding via Orthogonal Basis" paper implementation
https://github.com/fursovia/geometric_embedding

embeddings nlp

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"Zero-Training Sentence Embedding via Orthogonal Basis" paper implementation

• Host: GitHub
• URL: https://github.com/fursovia/geometric_embedding
• Owner: fursovia
• Created: 2018-12-07T01:05:42.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2018-12-23T08:46:01.000Z (almost 6 years ago)
• Last Synced: 2024-04-20T00:36:45.700Z (7 months ago)
• Topics: embeddings, nlp
• Language: Python
• Homepage:
• Size: 157 MB
• Stars: 19
• Watchers: 5
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

        
## Geometric Embedding Algorithm (GEM)

This is an implementation of Geometric Embedding Algorithm, a simple and robust non-parameterized approach for building sentence

representations. See the [paper](https://openreview.net/pdf?id=rJedbn0ctQ) for more details.

The work is done as a project for [NLA course](http://nla.skoltech.ru/) at Skoltech.

### Example

```python

from gem import SentenceEmbedder

from embeddings import get_embedding_matrix

sentences = ["We come from the land of the ice and snow",

            "From the midnight sun where the hot springs blow"]

            

embedding_matrix, vocab = get_embedding_matrix('glove.6B.300d.txt')

embedder = SentenceEmbedder(sentences, embedding_matrix, vocab)

embedded_sentences = embedder.gem(window_size=3, sigma_power=3)

```

### Data used

* [GloVe embeddings](https://nlp.stanford.edu/projects/glove/)

* [LexVec embeddings](https://github.com/alexandres/lexvec)

### Team

* [Alexey Bokhovkin](https://github.com/alexeybokhovkin)

* [Eugenia Cheskidova](https://github.com/fogside)

* [Ivan Fursov](https://github.com/fursovia)

* [Ruslan Rakhimov](https://github.com/rakhimovv)