glovpy

Package for interfacing Stanford's C GloVe implementation from Python.

Installation

Install glovpy from PyPI:

bash pip install glovpy

Additionally the first time you import glopy it will build GloVe from scratch on your system.

Requirements

We highly recommend that you use a Unix-based system, preferably a variant of Debian. The package needs git, make and a C compiler (clang or gcc) installed.

Otherwise the implementation is as barebones as it gets, only the standard library and gensim are being used (gensim only for producing KeyedVectors).

Example Usage

Here's a quick example of how to train GloVe on 20newsgroups using Gensim's tokenizer.

```python from gensim.utils import tokenize from sklearn.datasets import fetch_20newsgroups

from glovpy import GloVe

texts = fetch_20newsgroups().data corpus = [list(tokenize(text, lowercase=True, deacc=True)) for text in texts]

model = GloVe(vector_size=25) model.train(corpus)

for word, similarity in model.wv.most_similar("god"): print(f"{word}, sim: {similarity}") ```

| word | similarity | |------------|---------------| | existence | 0.9156746864 | | jesus | 0.8746870756 | | lord | 0.8555182219 | | christ | 0.8517201543 | | bless | 0.8298447728 | | faith | 0.8237065077 | | saying | 0.8204566240 | | therefore | 0.8177698255 | | desires | 0.8094088435 | | telling | 0.8083973527 |

API Reference

class glovpy.GloVe(vector_size, window_size, symmetric, distance_weighting, alpha, min_count, iter, initial_learning_rate, threads, memory)

Wrapper around the original C implementation of GloVe.

Parameters

| Parameter | Type | Description | Default | |------------------------|-------------------|--------------------------------------------------------------------------------------------------|------------------| | vectorsize | _int | Number of dimensions the trained word vectors should have. | 50 | | windowsize | _int | Number of context words to the left (and to the right, if symmetric is True). | 15 | | alpha | float | Parameter in exponent of weighting function; default 0.75 | 0.75 | | symmetric | bool | If true, both future and past words will be used as context, otherwise only past words will be used. | True | | distanceweighting | _bool | If False, do not weight cooccurrence count by distance between words. If True (default), weight the cooccurrence count by inverse of distance between the target word and the context word. | True | | mincount | _int | Minimum number of times a token has to appear to be kept in the vocabulary. | 5 | | iter | int | Number of training iterations. | 25 | | initiallearningrate | float | Initial learning rate for training. | 0.05 | | threads | int | Number of threads to use for training. | 8 | | memory | float | Soft limit for memory consumption, in GB. (based on simple heuristic, so not extremely accurate) | 4.0 |

Attributes

| Name | Type | Description | |------|------|-------------| | wv | KeyedVectors | Token embeddings in the form of Gensim keyed vectors. |

Methods

glovpy.GloVe.train(tokens)

Train the model on a stream of texts.

| Parameter | Type | Description | |-----------|------|-------------| | tokens | Iterable[list[str]] | Stream of documents in the form of lists of tokens. The stream has to be reusable, as the model needs at least two passes over the corpus. |

glovpy.utils.reusable(gen_func)

Function decorator that turns your generator function into an iterator, thereby making it reusable. You can use this if you want to reuse a generator function so that multiple passes can be made.

Parameters

| Parameter | Type | Description | |-----------|----------|----------------------------------------------| | genfunc | _Callable | Generator function that you want to be reusable. |

Returns

| Returns | Type | Description | |-----------|----------|--------------------------------------------------------| | multigen | _Callable | Iterator class wrapping the generator function. |

Example usage

Here's how to stream a very long file line by line in a reusable manner.

```python from gensim.utils import tokenize from glovpy.utils import reusable from glovpy import GloVe

@reusable def streamlines(): with open("verylongtextfile.txt") as f: for line in f: yield list(tokenize(line))

model = GloVe() model.train(stream_lines()) ```