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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
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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