BERT Large Uncased WWM SQuAD
| Property | Value |
|---|---|
| Parameter Count | 336M |
| Model Type | Question Answering |
| Architecture | 24-layer, 1024 hidden dim, 16 attention heads |
| F1 Score | 93.15 |
| Paper | arXiv:1810.04805 |
What is bert-large-uncased-whole-word-masking-finetuned-squad?
This is a specialized version of BERT large that employs whole word masking during pre-training and has been fine-tuned specifically for question answering tasks using the SQuAD dataset. The model represents a significant advancement in natural language processing, utilizing bidirectional training and achieving state-of-the-art performance in question answering tasks.
Implementation Details
The model implements a novel whole word masking technique where all tokens of a word are masked simultaneously during pre-training. It was trained on BookCorpus and English Wikipedia using 4 cloud TPUs in Pod configuration for one million steps with a batch size of 256. The model processes uncased text (no difference between "english" and "English") and uses WordPiece tokenization with a 30,000 token vocabulary.
- Pre-training uses MLM and NSP objectives with 15% masking rate
- Fine-tuned on SQuAD with learning rate of 3e-5
- Achieves 86.91 exact match score on evaluation
- Uses Adam optimizer with learning rate warmup and linear decay
Core Capabilities
- Advanced question answering on complex texts
- Bidirectional context understanding
- Robust performance on various text formats
- Efficient processing of long sequences up to 512 tokens
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its whole word masking approach during pre-training, where all tokens of a word are masked together, leading to more coherent language understanding. Combined with its large architecture and SQuAD fine-tuning, it achieves exceptional question answering performance.
Q: What are the recommended use cases?
This model is specifically optimized for question answering applications, making it ideal for building QA systems, information extraction tools, and automated customer support systems. It performs best when extracting specific answers from given contexts.
