update retrained model

README.md

@@ -22,7 +22,7 @@ widget:
 
 # EarningsCall2Vec
 
-**EarningsCall2Vec** is a [fastText](https://fasttext.cc/) word embedding model that was trained via [Gensim](https://radimrehurek.com/gensim/). It maps each token in the vocabulary to a dense, 300-dimensional vector space, designed for performing **semantic search**. More details about the training procedure can be found [below](#model-training).
+**EarningsCall2Vec** is a [`fastText`](https://fasttext.cc/) word embedding model that was trained via [`Gensim`](https://radimrehurek.com/gensim/). It maps each token in the vocabulary to a dense, 300-dimensional vector space, designed for performing **semantic search**. More details about the training procedure can be found [below](#model-training).
 
 
 ## Background
@@ -77,6 +77,9 @@ model.wv.most_similar(negative='transformation', topn=5, restrict_vocab=None)
 model.wv.similarity('transformation', 'continuity')
 ```
 
+If model size is crucial, the final model could be additionally compressed using the [`compress-fasttext`](https://github.com/avidale/compress-fasttext) library (e.g., via pruning, conversion to `float16`, or product quantization).
+
+
 ## Model Training
 
 The model has been trained on text data stemming from earnings call transcripts. The data is restricted to a call's question-and-answer (Q&A) section and the remarks by firm executives. The data has been preprocessed prior to model training via stop word removal, lemmatization, named entity masking, and coocurrence modeling.

model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 2556989501
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+size 2577407131