Visually Grounded embeddings for Fast-text and GloVe

This repository contains multiple visually grounded word embedding models. All of these embeddings have been effectively infused with visual information from images.
They have been proven to show stronger correlations (compared to textual embeddings) to human judgments on various word similarities and relatedness benchmarks.

Usage

All of the models are encoded in gensim format. Loading the model:

import gensim

model_g = gensim.models.KeyedVectors.load_word2vec_format('path_to_embeddings' , binary=True)

#retrieve the most similar words
print(model_g.most_similar('together',topn=10))

[('togther', 0.6425853967666626), ('togehter', 0.6374243497848511), ('togeather', 0.6196791529655457),
('togather', 0.5998020172119141), ('togheter', 0.5819681882858276),('toghether', 0.5738174319267273), 
('2gether', 0.5187329053878784), ('togethor', 0.501663088798523), ('gether', 0.49128714203834534), 
('toegther', 0.48457157611846924)]

print(model_g.most_similar('sad',topn=10))

[('saddening', 0.6763913631439209), ('depressing', 0.6676110029220581), ('saddened', 0.6352651715278625),
('sorrowful', 0.6336953043937683), ('heartbreaking', 0.6180269122123718), ('heartbroken', 0.6099187135696411),
('tragic', 0.6039361953735352), ('pathetic', 0.5848405361175537), ('Sad', 0.5826965570449829),
('mournful', 0.5742306709289551)]

#find the outlier word
print(model_g.doesnt_match(['fire', 'water', 'land', 'sea', 'air', 'car']))

car

where 'path_to_embeddings' is the path to the embeddings you intend to use.

Which embeddings to use

Under the Files and Versions tab, you can see the list of 4 available embeddings.

The following embedding files are from the paper Learning Zero-Shot Multifaceted Visually Grounded Word Embeddings via Multi-Task Training:

v_glove_1024d_1.0
v_fasttext_1024d_1.0

The following embedding files are from the paper Language with Vision: a Study on Grounded Word and Sentence Embeddings:

v_glove_1024d_2.0
v_glove_300_d_2.0

All of them come with 1024-dimensional word vectors except v_glove_300_d_2.0 which contains 300-dimensional word vectors.