DistilBERT Base Uncased Distilled SQuAD
| Property | Value |
|---|---|
| Parameter Count | 66.4M |
| License | Apache 2.0 |
| Paper | View Paper |
| F1 Score | 86.9 on SQuAD v1.1 |
What is distilbert-base-uncased-distilled-squad?
DistilBERT is a compressed version of BERT, designed to be smaller, faster, and more efficient while maintaining impressive performance. This particular model is specifically fine-tuned for Question Answering tasks using the SQuAD dataset. It achieves 95% of BERT's performance while being 40% smaller and running 60% faster.
Implementation Details
The model leverages knowledge distillation techniques to compress BERT's architecture while preserving its core capabilities. It was trained on BookCorpus and English Wikipedia data, then fine-tuned on SQuAD v1.1 for question answering tasks.
- Achieves 86.9 F1 score on SQuAD v1.1 dev set
- Requires 40% fewer parameters than bert-base-uncased
- Supports both PyTorch and TensorFlow implementations
- Trained using 8 16GB V100 GPUs for 90 hours
Core Capabilities
- Extractive Question Answering
- Context-based answer extraction
- Efficient processing with reduced computational requirements
- Multi-framework support (PyTorch/TensorFlow)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture that maintains high performance while significantly reducing size and increasing speed. It's particularly valuable for production environments where computational resources are limited.
Q: What are the recommended use cases?
The model is ideal for extractive question answering tasks where you need to find specific answers within a given context. It's particularly well-suited for applications requiring efficient processing of natural language queries against documented content.
