huggingface transformer training course, Mobile BERT fill-in-the-blank quiz

In this tutorial, we will create a fill-in-the-blank quiz using the Hugging Face Transformers library with the Mobile BERT model. Mobile BERT is a lightweight version of the BERT model designed for effective use in mobile environments, and it is utilized for various NLP tasks such as text embedding, question answering, and text classification.

1. Prerequisites

The environments and libraries required to proceed with this tutorial are as follows:

  • Python 3.6 or higher
  • Transformers library
  • torch library
  • pandas (optional, for using dataframes)

2. Environment Setup

!pip install transformers torch pandas

3. Introduction to Mobile BERT Model

Mobile BERT is a lightweight variant model based on BERT (Base Bidirectional Encoder Representations from Transformers) developed by Google. Mobile BERT utilizes the same architecture as BERT but has undergone several technical adjustments to reduce model size and increase execution speed. It is designed to support natural language processing tasks on mobile and edge devices.

4. Data Preparation

In this example, we will prepare text samples to construct fill-in-the-blank questions. The sample text will have specific words represented as blanks, and our goal is to find the most suitable word for those positions.

sample_text = "I love [MASK]. Machine learning is a type of [MASK]."

5. Loading the Mobile BERT Model

We will load the Mobile BERT model using Hugging Face’s Transformers library. Use the code below to import the model and tokenizer:


from transformers import MobileBertTokenizer, MobileBertForMaskedLM
import torch

# Load Mobile BERT tokenizer and model
tokenizer = MobileBertTokenizer.from_pretrained('google/mobilebert-uncased')
model = MobileBertForMaskedLM.from_pretrained('google/mobilebert-uncased')

6. Implementing the Fill-in-the-Blank Function

Now, we will implement a function that performs the fill-in-the-blank task. This function takes text as input, tokenizes a sentence containing [MASK], and returns the predicted results using the model.


def fill_mask(text):
    # Tokenize the text
    input_ids = tokenizer.encode(text, return_tensors='pt')
    
    # Model prediction
    with torch.no_grad():
        outputs = model(input_ids)
    
    # Get the predicted token IDs
    predictions = outputs.logits.argmax(dim=-1)
    
    # Restore text from predicted words
    filled_text = tokenizer.decode(predictions[0])
    return filled_text

7. Calling the Fill-in-the-Blank Function

Now let’s use the implemented function to perform the fill-in-the-blank task. Below is the code using a sample sentence with blanks.


# Sample text with blanks
sample_text = "I love [MASK]. Machine learning is a type of [MASK]."

# Call the fill-in-the-blank function
filled_text = fill_mask(sample_text)
print(filled_text)

8. Interpreting the Results

Interpreting the results predicted by the model. Mobile BERT is a pre-trained model with excellent performance in understanding the context of natural language and selecting appropriate words. This example helps us understand how the model fills in the blanks.

9. Practice: Fill-in-the-Blank for Multiple Sentences

Let’s practice filling in blanks for several sentences. Put multiple samples into a list and check the results using a loop.


# Multiple sentences with blanks
samples = [
    "I love [MASK].",
    "Machine learning is a type of [MASK].",
    "[MASK] is a very important concept."
]

# Fill in the blanks for each sample
for sample in samples:
    filled = fill_mask(sample)
    print(f"Original sentence: {sample} -> Filled sentence: {filled}")

10. Conclusion

In this tutorial, we addressed the NLP fill-in-the-blank problem utilizing Mobile BERT. By using Hugging Face’s Transformers library, complex natural language processing tasks can be performed easily. Mobile BERT operates efficiently in mobile environments, making it highly suitable for lightweight machine learning applications.

11. References