How to Use SpanBERT Fine-Tuned on SQuAD v2 for QA Tasks

May 24, 2021

Welcome to this comprehensive guide on utilizing SpanBERT, an impressive AI model from Facebook Research. Fine-tuned on the SQuAD 2.0 dataset, SpanBERT is tailored for robust question-answering (QA) applications. Let’s dive deep into the steps to implement this powerful tool effectively!

Understanding SpanBERT

SpanBERT enhances pre-training by adeptly representing and predicting spans of text. This ability is crucial when working with datasets that demand not just answering questions but also discerning when a response is unavailable, a key feature introduced with the SQuAD 2.0 dataset.

What is SQuAD 2.0?

SQuAD 2.0 combines over 100,000 questions from SQuAD 1.1 with an additional 50,000 unanswerable questions, compelling models to identify when no answer exists. This is akin to a quiz that not only tests your knowledge but also includes trick questions meant to confuse you, making it necessary for the model to think critically.

Ready to Train! Here’s How to Do It:

Follow these steps to set up and fine-tune the SpanBERT model on SQuAD 2.0:

Install necessary libraries (if you haven’t already)
Download the train and evaluation files for SQuAD 2.0
Run the fine-tuning command:

bash
python run_squad.py \
  --do_train \
  --do_eval \
  --model spanbert-large-cased \
  --train_file train-v2.0.json \
  --dev_file dev-v2.0.json \
  --train_batch_size 32 \
  --eval_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 4 \
  --max_seq_length 512 \
  --doc_stride 128 \
  --eval_metric best_f1 \
  --output_dir squad2_output \
  --version_2_with_negative \
  --fp16

Understanding the Fine-Tuning Process

Imagine SpanBERT as a student in a classroom. Initially, this student has an excellent understanding of the subject (text spans) but needs to take tests (fine-tuning) to improve specific skills (QA). Each parameter in the command enhances the student’s ability to understand and answer questions better:

–train_file & –dev_file: The textbooks (datasets) used for studying.
–train_batch_size: The number of questions studied at once.
–learning_rate: The speed at which the student learns from mistakes.
–num_train_epochs: The number of times the student goes through the same material.
–max_seq_length: The maximum length of the questions the student can comprehend.

Evaluating Results

After successfully training your model, you can evaluate it using the following comparison metrics:

Model	F1 Score (SQuAD 1.1)	F1 Score (SQuAD 2.0)	Average F1 (Coref)
SpanBERT (large)	94.6	88.7	70.8

Using SpanBERT for Quick Predictions

To leverage the model for real-time question-answering, simply implement the following code:

python
from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="mrm8488/spanbert-large-finetuned-squadv2",
    tokenizer="SpanBERT/spanbert-large-cased"
)

qa_pipeline({
    'context': "Manuel Romero has been working very hard in the repository lately.",
    'question': "How has Manuel Romero been working lately?"
})
# Output: answer: very hard, end: 40, score: 0.905...

Troubleshooting Common Issues

If you encounter any issues while fine-tuning or using the model, consider the following troubleshooting steps:

Ensure all required libraries are properly installed.
Verify that the dataset paths are correct and that the JSON files are undamaged.
Adjust the learning rate or batch size if the training doesn’t converge.
Restart the kernel/environment if facing Python-related errors.

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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.