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Jen Looper authoredJen Looper authored
Language Modeling
Semantic embeddings, such as Word2Vec and GloVe, are in fact a first step towards language modeling - creating models that somehow understand (or represent) the nature of the language.
Pre-lecture quiz
The main idea behind language modeling is training them on unlabeled datasets in an unsupervised manner. This is important because we have huge amounts of unlabeled text available, while the amount of labeled text would always be limited by the amount of effort we can spend on labeling. Most often, we can build language models that can predict missing words in the text, because it is easy to mask out a random word in text and use it as a training sample.
Training Embeddings
In our previous examples, we used pre-trained semantic embeddings, but it is interesting to see how those embeddings can be trained using either CBoW, or Skip-gram architectures.
Image from this paper
The idea underpinning CBoW involves how to predict a missing word, but to do this we take a small sliding window of text tokens. We can denote them from W-2 to W2, and train a model to predict the central word W0 from a few surrounding words.
Conclusion
TBD
TBD
Post-lecture quiz
Review & Self Study
- Official PyTorch tutorial on Language Modeling.
- Official TensorFlow tutorial on training Word2Vec model.
- Using the gensim framework to train most commonly used embeddings in a few lines of code is described in this documentation.