Welcome to our tutorial on how to utilize the fine-tuned RoBERTa QA model for extractive question answering. This model is specifically designed to answer questions based on a given context, particularly effective for the Japanese language, with training sourced from Japanese Wikipedia.

Why Use RoBERTa QA?

The RoBERTa QA model, developed by rinna Co., Ltd., offers enhanced performance in question answering tasks. By fine-tuning the base model on the JaQuAD dataset, it has learned to extract precise answers from Japanese texts. Let’s dive into how to set it up!

How to Set Up and Use RoBERTa QA

Here, we will walk through two main methods for utilizing the model: running it through a dedicated pipeline and manually executing it.

Method 1: Using a Dedicated Pipeline

If you prefer a simpler, streamlined approach, you can use a dedicated pipeline from the transformers library. Follow these steps:

from transformers import pipeline

model_name = "tsmatz/roberta_qa_japanese"
qa_pipeline = pipeline(
    "question-answering",
    model=model_name,
    tokenizer=model_name
)
result = qa_pipeline(
    question="What is AI?",
    context="AI stands for Artificial Intelligence and is used for ..."
)
print(result)

Method 2: Manually Running Inference

For more control, you can manually run the model through a forward pass.

import torch
import numpy as np
from transformers import AutoModelForQuestionAnswering, AutoTokenizer

model_name = "tsmatz/roberta_qa_japanese"
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def inference_answer(question, context):
    test_feature = tokenizer(question, context, max_length=318, return_tensors='pt')
    with torch.no_grad():
        outputs = model(**test_feature)
    start_logits = outputs.start_logits.cpu().numpy()
    end_logits = outputs.end_logits.cpu().numpy()
    answer_ids = test_feature["input_ids"][0][np.argmax(start_logits):np.argmax(end_logits) + 1]
    return tokenizer.decode(answer_ids)

question = "What is AI?"
context = "AI stands for Artificial Intelligence and is used for ..."
answer_pred = inference_answer(question, context)
print(answer_pred)

Understanding the Code

You can think of this code as a chef preparing a special dish:

• Ingredients (model & tokenizer): We first gather our ingredients, which include the model and tokenizer.
• Preparation (inference function): We set up our cooking process by defining the inference_answer function, much like prepping ingredients before cooking.
• The Cooking (forward pass): Within this function, we cook the question and context together to extract the answer, similar to simmering ingredients until the flavors blend.
• Serving (printing the answer): Finally, we serve the finished dish — the predicted answer!

Troubleshooting Tips

If you encounter issues while using the RoBERTa QA model, here are some troubleshooting ideas:

• Issue: Model not loading or throwing an error.
  Solution: Ensure you have installed the necessary libraries, including transformers and torch.
• Issue: Unclear results or unexpected answers.
  Solution: Verify your input context and question. Adjust them to ensure clarity.
• Issue: Memory errors during inference.
  Solution: If using a local setup, consider lowering the input size or utilizing a device with more resources.

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Further Learning

For those interested in the training process of the RoBERTa model, the source code for fine-tuning is available here.

Conclusion

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.