How to Preprocess data using transformers?

This recipe helps you to preprocess data using transformers.

Recipe Objective - How to Preprocess data using transformers?

A tokenizer is the most important tool for preprocessing of data. You can create one by utilizing the tokenizer class related to the model you would like to utilize, or by using the AutoTokenizer class directly. The tokenizer will separate a given text into tokens (words or parts of words, punctuation symbols, etc.). It will then transform those tokens into numbers so that it can construct a tensor out of them and feed it to the model. It will also provide any other inputs that the model may require to function effectively.

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Example:

# Importing libraries
from transformers import AutoTokenizer

# Loading model
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')

# Passing input to model
encoded_value = tokenizer("Hello world!")

# Printing the tokens(encoded values)
print(encoded_value)

# Decoding the encoded values to get input back
tokenizer.decode(encoded_value["input_ids"])

Output - 
{'input_ids': [101, 8667, 1362, 106, 102], 'token_type_ids': [0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1]}
'[CLS] Hello world! [SEP]'

In this way, we can preprocess data using transformers.

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