Question answering using transformers and BERT

Huggingface transformer has a pipeline called question answering we will use it here.

Question answering pipeline uses a model finetuned on Squad task.

Let’s see it in action.

1. Install Transformers library in colab.

!pip install transformers

or, install it locally,

pip install transformers

2. Import transformers pipeline,

from transformers import pipeline

3. Set the pipeline.

nlp_qa = pipeline('question-answering')

4. Pass the context and question to the pipeline.

nlp_qa(context='Google, LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, a search engine, cloud computing, software, and hardware.Google corporate headquarters located at Mountain View, California, United States.', 
       question='Where is based Google ?')

Which shows the output,

{'answer': 'Mountain View, California,',
 'end': 291,
 'score': 0.4971035420894623,
 'start': 265}

Now, let’s try a different model.

We will use a tiny transformer model called bert-tiny-finetuned-squadv2

Set the bert-tiny model in model and tokenizer as below,

# Alternative:# Alternative:
from transformers import *
tokenizer = AutoTokenizer.from_pretrained("mrm8488/bert-tiny-5-finetuned-squadv2")
model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/bert-tiny-5-finetuned-squadv2")

bert_tiny_nlp_qa = pipeline('question-answering', 
                            model = model,
                            tokenizer = tokenizer)

Pass the same question and context to the bert_tiny_nlp_qa pipeline.

bert_tiny_nlp_qa(context='Google, LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, a search engine, cloud computing, software, and hardware.Google corporate headquarters located at Mountain View, California, United States.', 
       question='Where is based Google ?')

which shows following output,

{'answer': 'Mountain View, California, United States.',
 'end': 305,
 'score': 0.5947868227958679,
 'start': 265}

As you can see both are giving same answer.

%timeit nlp_qa(context='Google, LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, a search engine, cloud computing, software, and hardware.Google corporate headquarters located at Mountain View, California, United States.', question='Where is based Google ?')

%timeit bert_tiny_nlp_qa(context='Google, LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, a search engine, cloud computing, software, and hardware.Google corporate headquarters located at Mountain View, California, United States.', question='Where is based Google ?')

output:

1 loop, best of 3: 233 ms per loop

As, you can see bert-tiny is 3.75 times faster than the default pipeline.