https://github.com/krzjoa/salto

Playing with embedding vectors
https://github.com/krzjoa/salto

nlp python visulaization word-embeddings

Last synced: about 2 months ago
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Playing with embedding vectors

• Host: GitHub
• URL: https://github.com/krzjoa/salto
• Owner: krzjoa
• License: mit
• Created: 2022-05-15T19:57:00.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-11-21T21:11:55.000Z (about 1 year ago)
• Last Synced: 2024-10-29T01:29:49.999Z (about 2 months ago)
• Topics: nlp, python, visulaization, word-embeddings
• Language: Python
• Homepage: https://salto.readthedocs.io
• Size: 3.44 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# salto 

> Playing with embedding vectors

## Installation

You can install this library from *PyPI*

```bash

pip install salto

```

or from the GitHub repo:

 

```bash

pip install git+https://github.com/krzjoa/salto.git

```

## Motivation

The goal of the **salto** package is to explore embeddings and check, 

how the distance between two points (vectors) can be interpreted.

We get two arbitrary selected points, such as embedding vectors for **ice** and **fire**

draw a straight line passing trough the both these points. Then, we treat the 

newly created line as a new axis by projecting the rest of the points onto this line.

 



 Drawn using: https://www.geogebra.org/m/JMMKv7cx

 

I named the package **salto**, which means *somersault* in many languages or simply *jump* in Romance languages like Italian, where this word originally comes from.

It's because the operation of changing space for me resembles a kind of acrobatics 😉.

## Usage

```python

import numpy as np

import spacy

import salto

nlp = spacy.load('en_core_web_md')

fire = nlp('fire')

ice = nlp('ice')

ice_fire_axis = salto.axis(ice.vector, fire.vector)

cold = ['ice cream', 'polar', 'snow', 'winter', 'fridge', 'Antarctica']

warm = ['boiling water', 'tropical', 'sun', 'summer', 'oven', 'Africa']

cold_vecs = [nlp(w).vector for w in cold]

warm_vecs = [nlp(w).vector for w in warm]

cold_values = [ice_fire_axis(p) for p in cold_vecs]

warm_values = [ice_fire_axis(p) for p in warm_vecs]

ice_fire_axis.plot(

        {'values': cold_values, 'labels': cold, 'color': 'tab:blue'},

        {'values': warm_values, 'labels': warm, 'color': 'tab:red'},

        poles = {'negative': {'label': 'ice', 'color': 'blue'}, 

                 'positive': {'label': 'fire', 'color': 'red'}}

    )  

```



## See also

[scikit-spatial](https://github.com/ajhynes7/scikit-spatial) - Spatial objects and computations based on NumPy arrays.