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@@ -83,7 +83,7 @@ vectorizer = Vectorizer(pretrained_weights='distilbert-base-multilingual-cased')
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### 2. How to use Word2Vec model?
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If you want to use a Word2Vec approach instead, you must pass a valid path to the model weights. Under the hood, the sentences will be split into lists of words using the `sent2words` method from the `Splitter` class. It is possible to customize the list of stop-words by adding or removing to/from the default list. Two additional arguments (both lists) must be passed when the vectorizer's method .run is called: `remove_stop_words` and `add_stop_words`.
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NOTE: The default method to computes the sentence embeddings after extracting list of vectors is average of vectors corresponding to the remaining words.
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Note: The default method to computes the sentence embeddings after extracting list of vectors is average of vectors corresponding to the remaining words.
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```python
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from sent2vec.vectorizer import Vectorizer

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