Welcome to this user-friendly guide that helps you add a sprinkle of intelligence to your applications using a ParsBERT-based model for multiple-choice question answering! With this model, you can effortlessly generate answers from a given set of candidates. Let’s dive into the implementation and the magic it can create.

What is ParsBERT?

ParsBERT is a pretrained language model designed specifically for the Persian language. It can perform various natural language tasks, including text classification and question answering. In this case, we’re using it for a multiple-choice question-answering scenario.

Getting Started

First, let’s lay the groundwork with the necessary libraries. You will need to have Python and the ‘transformers’ library installed. If you haven’t installed the transformers library yet, you can do so using pip:

pip install transformers

Setting Up the Model

Here’s how to run the model in just a few simple steps:

from typing import List
import torch
from transformers import AutoConfig, AutoModelForMultipleChoice, AutoTokenizer

model_name = "persiannlp/parsbert-base-parsinlu-multiple-choice"
tokenizer = AutoTokenizer.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
model = AutoModelForMultipleChoice.from_pretrained(model_name, config=config)

Understanding the Code: An Analogy

Think of multiple-choice question answering like a quiz game. You have a quiz master (your model) and four contestants (possible answers). The quiz master needs the question and the four answers to decide which contestant is correct. The setup includes:

• Tokenization: This is similar to explaining the rules to each contestant before they answer.
• Input Preparation: Just like how contestants practice with questions, we prepare our inputs by encoding them.
• Model Inference: This is where the quiz master evaluates the contestants and announces the winner.

Running the Model

Now, let’s create a function to run the model:

def run_model(question: str, candidates: List[str]):
    assert len(candidates) == 4, "You need four candidates"
    choices_inputs = []
    for c in candidates:
        text_a = ""  # empty context
        text_b = question + " " + c
        inputs = tokenizer(
            text_a,
            text_b,
            add_special_tokens=True,
            max_length=128,
            padding="max_length",
            truncation=True,
            return_overflowing_tokens=True,
        )
        choices_inputs.append(inputs)
    
    input_ids = torch.LongTensor([x["input_ids"] for x in choices_inputs])
    output = model(input_ids=input_ids)
    print(output)
    return output

Examples of Using the Model

Now that we have our functions set up, let’s run some example queries:

run_model(question="وسیع ترین کشور جهان کدام است؟", candidates=["آمریکا", "کانادا", "روسیه", "چین"])
run_model(question="طامع یعنی ؟", candidates=["آزمند", "خوش شانس", "محتاج", "مطمئن"])
run_model(question="زمینی به ۳۱ قطعه متساوی مفروض شده است و هر روز مساحت آماده شده برای احداث، دو برابر مساحت روز قبل است. اگر پس از (۵ روز) تمام زمین آماده شده باشد، در چه روزی یک قطعه زمین آماده شده؟", candidates=["روز اول", "روز دوم", "روز سوم", "هیچکدام"])

Troubleshooting

If you run into any issues while implementing the model, try these troubleshooting tips:

• Ensure you have all necessary libraries installed and updated.
• Check for errors in inputs; the function requires four candidate answers for every question.
• If the model fails to load, verify your internet connection and try again.

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Conclusion

In this article, you’ve learned how to implement a ParsBERT-based model for multiple-choice question answering. Such innovative solutions are vital as we push the boundaries in AI technology.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.