Explain AlBert and its working with the help of an example?
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Explain AlBert and its working with the help of an example?

Explain AlBert and its working with the help of an example?

This recipe explains AlBert and its working with the help of an example

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Recipe Objective

Explain AlBert and it's working with the help of an example.

Albert is an "A lit BERT" for self-supervised learning language representation, it is an upgrade to BERT that offers improved performance on various NLP tasks. ALBERT reduces model sizes in two ways - by sharing parameters across the hidden layers of the network, and by factorizing the embedding layer.

Step 1 - Install the required library

!pip install transformers

Step 2 - Albert Configuration

from transformers import AlbertConfig, AlbertModel albert_configuration_xxlarge = AlbertConfig() albert_configuration_base = AlbertConfig( hidden_size=768, num_attention_heads=12, intermediate_size=3072, )

Here we are configuring the Albert from the transformer library, The first step is about initializing the ALBERT-xxlarge style configuration, after that we are initializing the ALBERT-base style configuration, then initialize the model

Step 2 - Albert Tokenizer

from transformers import AlbertTokenizer, AlbertModel import torch albert_tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') albert_model = AlbertModel.from_pretrained('albert-base-v2', return_dict=True)

Step 3 - Print the Results

Sample = tokenizer("Hi everyone your learning NLP", return_tensors="pt") Results = albert_model(**Sample) last_hidden_states = Results.last_hidden_state print(last_hidden_states)
tensor([[[ 2.4208,  1.8559,  0.4701,  ..., -1.1277,  0.1012,  0.7205],
         [ 0.2845,  0.7017,  0.3107,  ..., -0.1968,  1.9060, -1.2505],
         [-0.5409,  0.8328, -0.0704,  ..., -0.0470,  1.0203, -1.0432],
         ...,
         [ 0.0337, -0.5312,  0.3455,  ...,  0.0088,  0.9658, -0.8649],
         [ 0.2958, -0.1336,  0.6774,  ..., -0.1669,  1.6474, -1.7187],
         [ 0.0527,  0.1355, -0.0434,  ..., -0.1046,  0.1258,  0.1885]]],
       grad_fn=)

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