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attention-imgs
attention-imgs
 
 
conf_levels
conf_levels
 
 
confusion_mats
confusion_mats
 
 
errors
errors
 
 
hyperparams
hyperparams
 
 
intent-error-categories
intent-error-categories
 
 
learning-curves-plots
learning-curves-plots
 
 
results
results
 
 
slot-errors
slot-errors
 
 
README.md
README.md
 
 
__init__.py
__init__.py
 
 
auto_train_test.ipynb
auto_train_test.ipynb
 
 
capsnet-i2s.ipynb
capsnet-i2s.ipynb
 
 
capsnet-s2i.ipynb
capsnet-s2i.ipynb
 
 
conf_histograms.py
conf_histograms.py
 
 
conf_levels_generator.py
conf_levels_generator.py
 
 
convert_all_datasets.py
convert_all_datasets.py
 
 
convert_capsnets_format.py
convert_capsnets_format.py
 
 
data_loader.py
data_loader.py
 
 
demo.ipynb
demo.ipynb
 
 
errors_subsampling.py
errors_subsampling.py
 
 
flags.py
flags.py
 
 
high-level-arch.png
high-level-arch.png
 
 
html_report_generator.py
html_report_generator.py
 
 
learning-curves.py
learning-curves.py
 
 
model_i2s.py
model_i2s.py
 
 
model_s2i.py
model_s2i.py
 
 
requirements.txt
requirements.txt
 
 
result-styles.css
result-styles.css
 
 
test.py
test.py
 
 
tf_example.py
tf_example.py
 
 
train.py
train.py
 
 
util.py
util.py
 
 
word_vector_cleanup.py
word_vector_cleanup.py
 
 

README.md

Loading faster the fasttext models

There are 2 types of files you can download from fasttext

.vec - file, imported like:

from gensim.models.keyedvectors import KeyedVectors

w2v = KeyedVectors.load_word2vec_format(file_name, binary=False)

.bin - file, imported like:

from gensim.models import FastText

w2v = FastText.load_fasttext_format(file_name)
w2v.wv # use it like this

The latter method loads the word vector model in ~ 130 - 150 seconds on my machine while the first method loads the vectors in around 10 mins.