bert-large-finetuned-squad2
| Property | Value |
|---|---|
| Base Model | BERT Large Uncased |
| Research Paper | Available Here |
| Dataset | SQuAD2.0 |
| Performance | 76.22% Exact Match, 79.73% F1 |
What is bert-large-finetuned-squad2?
bert-large-finetuned-squad2 is a specialized question-answering model built on the BERT Large architecture and fine-tuned on the SQuAD2.0 dataset. This model represents a significant advancement in natural language processing, specifically designed for extractive question-answering tasks.
Implementation Details
The model is implemented using the transformers library and has been carefully fine-tuned with specific hyperparameters including a learning rate of 3e-5, 4 training epochs, and a maximum sequence length of 384 tokens. The training process utilized a batch size of 96 and implemented document stride of 128 tokens.
- Built on bert-large-uncased architecture
- Optimized for both answerable and unanswerable questions
- Supports maximum query length of 64 tokens
- Implements sliding window approach with 128 token stride
Core Capabilities
- Extractive Question Answering
- Handles both answerable and no-answer scenarios
- Achieves 76.22% exact match accuracy on SQuAD2.0
- Balanced performance across answer types (HasAns: 76.20%, NoAns: 76.25%)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its robust performance on SQuAD2.0, particularly its balanced handling of both answerable and unanswerable questions, making it ideal for real-world applications where not all questions have answers in the given context.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring extractive question answering capabilities, such as document analysis, automated customer support, and information retrieval systems. It's especially valuable when dealing with scenarios where determining whether a question is answerable is as important as finding the answer itself.
