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This page discusses some properties of the GensimFeaturizer. Note that this featurizer is a dense featurizer.

Gensim is a popular python library that makes it relatively easy to train your own word vectors. This can be useful if your corpus is very different than what most popular embeddings are trained on. We'll give a small guide on how to train your own embeddings here but you can also read the guide on the gensim docs.

Training Your Own

Training your own gensim model can be done in a few lines of code. A demonstration is shown below.

from gensim.models import Word2Vec

# Gensim needs a list of lists to represent tokens in a document.
# In real life you’d read a text file and turn it into lists here.
text = ["this is a sentence", "so is this", "and we're all talking"]
tokens = [t.split(" ") for t in text]

# This is where we train new word embeddings.
model = Word2Vec(sentences=tokens, size=10, window=3,
                 min_count=1, iter=5, workers=2)

# This is where they are saved to disk."wordvectors.kv")

This wordvectors.kv file should contain all the vectors that you've trained. It's this file that you can pass on to this component.

Configurable Variables

  • cache_path: pass it the name of the filepath where you've downloaded/saved the embeddings

Base Usage

The configuration file below demonstrates how you might use the gensim embeddings. In this example we're building a pipeline for the English language and we're assuming that you've trained your own embeddings which have been saved upfront as saved/beforehand/filename.kv.

language: en

- name: WhitespaceTokenizer
- name: LexicalSyntacticFeaturizer
- name: CountVectorsFeaturizer
  analyzer: char_wb
  min_ngram: 1
  max_ngram: 4
- name: rasa_nlu_examples.featurizers.dense.GensimFeaturizer
  cache_path: path/to/filename.kv
- name: DIETClassifier
  epochs: 100