A Go library implementing word embedding models (Word2Vec, GloVe, LexVec) from scratch with CLI and SDK.
wego is a Go library that provides from-scratch implementations of popular word embedding models, including Word2Vec, GloVe, and LexVec. It enables developers to transform words into meaningful vector representations by training models on custom text corpora and performing semantic similarity searches and vector arithmetic operations.
Go developers and researchers working on natural language processing (NLP) tasks who need to generate or experiment with word embeddings from custom datasets. It is also suitable for those interested in understanding or modifying the underlying algorithms of word embedding models.
Developers choose wego for its clean, from-scratch implementations in Go, which offer performance and usability for NLP tasks, along with a CLI tool for easy training and querying and an SDK for programmatic control with functional options for hyperparameter configuration.
Word Embeddings in Go!
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Implements Word2Vec, GloVe, and LexVec from scratch, providing a range of foundational algorithms for word embedding tasks.
Utilizes HogWild! asynchronous optimization for faster model training on large datasets, as mentioned in the README.
Offers both a CLI for straightforward training and querying, and a Go SDK with functional options for detailed hyperparameter tuning.
Includes a console REPL for performing word vector operations like addition and subtraction, enabling exploratory analysis of semantic relationships.
Users must train models from scratch, which can be time-consuming and requires substantial computational resources and data preparation.
Corpora must be formatted as space-separated words, limiting flexibility with other text preprocessing methods or dataset formats.
The HogWild! algorithm leads to non-deterministic training, making it unsuitable for scenarios where consistent results are essential.