Frequency-Agnostic
Code for NIPS 2018 paper 'Frequency-Agnostic Word Representation'
AI summary
Word embedding trainer
Improves word embeddings by training with adversarial objectives
- stars
- 118
- forks
- 20
- watching
- 6
Similar projects
Found by comparing what the projects do, not just their names.
Language embeddings
Provides pre-trained ELMo representations for multiple languages to improve NLP tasks.
Word embeddings trainer
Trains word embeddings from a paraphrase database to represent semantic relationships between words.
N-gram embedder
A tool for training and using character n-gram based word and sentence embeddings in natural language processing.
NLP embedders
Develops unified sentence embedding models for NLP tasks
Embeddings
Provides fast and efficient word embeddings for natural language processing.
Word embedding model
A Python implementation of a neural network model for learning word embeddings from text data
Word embedder
A deep learning model that generates word embeddings by predicting words based on their dependency context
Language vectors
Provides pre-trained word vectors for multiple languages to facilitate NLP tasks
Word embeddings library
This implementation provides a way to represent words as multivariate Gaussian distributions, allowing scalable word embeddings.
Word embedding model
A PyTorch implementation of the skip-gram model for learning word embeddings.
N-gram embeddings
A repository providing a re-implementation of character n-gram embeddings for pre-training in natural language processing tasks
Embedding transformer
Transforms existing word embeddings into more interpretable ones by applying a novel extension of k-sparse autoencoder with stricter sparsity constraints
Character Glyph Embedding
A software implementation of a word embedding method using character glyphs, enhancing traditional Chinese language processing
Ngram Embedder
A toolkit for learning high-quality word and text representations from ngram co-occurrence statistics
Stereotype analysis
Quantifies 100 years of gender and ethnic stereotypes through word embeddings.