BERT-QA
Building a machine reading comprehension system using pretrained model bert.
Quick Start
1. Prepare your training data and install the package in requirement.txt
2. Fine-tune BERT model
sh tarin.sh
3. Interaction
sh interaction.sh
Experiment
Input context format like below：
{ "sentence":"YOUR_SENTENCE。", "question":"YOUR_QUESTION"}
The experimental result of F1-measure：
Evaluation 100%|███████████████████████████████████| 495/495 [00:05<00:00, 91.41it/s]
Average f1 : 0.5596300989105781
Display Result
Model architectures
BERT (from Google) released with the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
Dataset
In this experiments, we use the datasets from DRCKnowledgeTeam. (https://github.com/DRCKnowledgeTeam/DRCD)