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Getting Started with Google BERT

ALBERT : Embeddings Extraction

Explore how to use ALBERT for extracting contextual word embeddings in sentences with Hugging Face transformers. Learn to preprocess inputs, load ALBERT’s pretrained model and tokenizer, and retrieve hidden states for NLP applications.

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With Hugging Face's transformers, we can use the ALBERT model just like how we used BERT. Let's explore this with a small example.

Suppose we need to get the contextual word embedding of every word in the sentence: 'Paris is a beautiful city'. Let's see how to do that with ALBERT.

Import the necessary modules

Let's first import the necessary modules:

from transformers import AlbertTokenizer, AlbertModel

Loading the model and tokenizer

Now, we download and load the pre-trained ALBERT model and tokenizer. We'll use the ALBERT-base model:

model = AlbertModel.from_pretrained('albert-base-v2')
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
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