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Extractive Question Answering Not Evaluated
This model is a DistilBERT model fine-tuned on the SQuAD dataset for extractive question answering tasks, with high exact match rate and F1 score.
Downloads 18
Release Time : 12/2/2022
Model Overview
This model is a DistilBERT-based extractive question answering model, fine-tuned on the SQuAD dataset, capable of extracting answers from given texts to answer user questions.
Model Features
Efficient Inference
The model features low latency (0.0086 seconds/sample) and high throughput (116 samples/second).
Lightweight Architecture
Based on DistilBERT, it is 40% smaller than the original BERT model while retaining 97% of its performance.
Excellent Performance
Achieves 72.95% exact match rate and 81.86% F1 score on the SQuAD evaluation set.
Model Capabilities
Text Understanding
Answer Extraction
Question Answering System
Use Cases
Education
Reading Comprehension Assistance
Helps students quickly find answers to questions from texts.
Improves learning efficiency and reduces search time.
Customer Service
FAQ Auto-Response
Automatically extracts answers from knowledge base documents to respond to customer queries.
Reduces workload for human customer service.
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