https://github.com/cadmiumcr/glove
Pure Crystal implementation of Global Vectors for Word Representation (GloVe)
https://github.com/cadmiumcr/glove
cadmium crystal global-vectors glove machine-learning ml natural-language-processing nlp word-embeddings
Last synced: 6 months ago
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Pure Crystal implementation of Global Vectors for Word Representation (GloVe)
- Host: GitHub
- URL: https://github.com/cadmiumcr/glove
- Owner: cadmiumcr
- License: mit
- Created: 2019-06-18T06:34:33.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-08-27T21:22:12.000Z (about 6 years ago)
- Last Synced: 2025-03-24T17:13:39.398Z (7 months ago)
- Topics: cadmium, crystal, global-vectors, glove, machine-learning, ml, natural-language-processing, nlp, word-embeddings
- Language: Crystal
- Homepage:
- Size: 356 KB
- Stars: 3
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Cadmium::Glove
Pure Crystal implementation of Global Vectors for Word Representations.
> Note that this does not work quite right yet. Something is off with the math and it's returning incorrect results.
# Overview
GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.
# Resources
- [Documentation](http://www.rubydoc.info/github/vesselinv/glove)
- [Academic Paper on Global Vectors](http://nlp.stanford.edu/projects/glove/glove.pdf)
- [Original C Implementation](http://nlp.stanford.edu/projects/glove/)
#### Implementations in other languages
- [ruby](https://github.com/vesselinv/glove)
- [python](https://github.com/maciejkula/glove-python)
- [scala](https://github.com/petro-rudenko/spark-glove)
## Installation
1. Add the dependency to your `shard.yml`:
```yaml
dependencies:
cadmium_glove:
github: cadmium_cr/glove
```
2. Run `shards install`
## Usage
```crystal
require "cadmium"
require "cadmium_glove"
include Cadmium
# Create a new model. Values used here are the defaults.
model = Glove::Model.new(
max_count: 100,
learning_rate: 0.05,
alpha: 0.75,
num_components: 30,
epochs: 5
)
# Feed the model some text
text = File.read("quantum-physics.txt")
model.fit(text)
# Alternatively you can pass the model a Corpus object
corpus = Glove::Corpus.build(text)
model.fit(corpus)
# Train the model
model.train
# Save the model as JSON
model.save("./data")
```
To import and use a model:
```crystal
# Load the previously saved model from the data directory
model = Glove::Model.load("./data")
# Get the most similar words
puts model.most_similar("quantum")
# => [["physics", 0.9974459436353388], ["mechanics", 0.9971606266531394], ["theory", 0.9965966776283189]]
# Find words that are releated to atom like quantum is related to physics
puts model.analogy_words("atom", "quantum", "physics")
# => [["electron", 0.9858380292886947], ["energie", 0.9815122410243475], ["photon", 0.9665073849076669]]
```
## Performance
TODO: Benchmarks
## Contributing
1. Fork it ()
2. Create your feature branch (`git checkout -b my-new-feature`)
3. Commit your changes (`git commit -am 'Add some feature'`)
4. Push to the branch (`git push origin my-new-feature`)
5. Create a new Pull Request
## Contributors
- [Chris Watson](https://github.com/watzon) - creator and maintainer