---
license: mit
language:
- en
---
# 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.<br>
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](https://pypi.org/project/gensim/) format.
Loading the model:
```python
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](https://aclanthology.org/2021.conll-1.12/):
- 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](https://arxiv.org/pdf/2206.08823.pdf):

- 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 1024-dimensional word vectors.