[파이썬] Gensim에서의 Hierarchical Softmax

06 Sep 2023

gensim

In natural language processing, hierarchical softmax is a technique used to efficiently train and predict probabilities for large vocabularies. It is commonly used in word embeddings models such as Word2Vec.

Gensim is a popular Python library for topic modeling and document similarity analysis. It provides an implementation of hierarchical softmax that can be easily integrated into your NLP pipeline.

To use hierarchical softmax in Gensim, follow these steps:

Install Gensim: If you haven’t already installed Gensim, you can do so by running the following command:
```
pip install gensim
```

Import the necessary libraries:

from gensim.models import Word2Vec
from gensim.models.word2vec import LineSentence

Prepare your corpus: First, you need to preprocess your text corpus and save it as a text file, where each line contains a single sentence. For example, you can save your corpus as “corpus.txt”.
Train the Word2Vec model with hierarchical softmax:
```
model = Word2Vec(sentences=LineSentence("corpus.txt"), hs=1)
```
In this example, we pass the “corpus.txt” file path as input to the LineSentence class, which reads the file line by line. We set the hs parameter to 1 to enable hierarchical softmax.
Use the trained model to predict word probabilities:
```
word = "example"
probabilities = model.predict_output_word([word])
```
Given a target word, the predict_output_word method returns a list of tuples, where each tuple contains a word and its corresponding probability. In this example, we provide the word “example” as input.

By integrating hierarchical softmax into your word embeddings models, you can speed up training and prediction for large vocabularies. Gensim’s implementation makes it easy to incorporate this technique into your NLP projects.

Remember to preprocess your corpus appropriately, including tokenization, lowercasing, and removing stop words, before training the Word2Vec model. This will help improve the quality of your word embeddings and the accuracy of the predicted probabilities.