Hello,
I was testing the BERT SQUAD infer config and as I understand it was not trained on data with no answer, right?
Do you have models or configs for running BERT SQUAD infer trained with no answer (squad 2.0)?
Thanks,
Yaniv.
Hi!
Yes, squad_bert_infer.json model was not trained on data with no answer, but examples with no answer could appear during training process: if paragraph is too long we cut it to 384 subtokens (question + paragraph + special tokens). So, squad_bert model is able to deal with no answer questions (it uses [CLS] token as no answer) , but it is better to specially train it on such kind of data.
If you need model trained on data with no answer you can try multi_squad_noans_infer.json config. This model is based on R-Net and data used for training is described here: http://docs.deeppavlov.ai/en/master/features/models/squad.html#squad-with-contexts-without-correct-answers . You can also train BERT-based model on this data.
We don’t have pre-trained BERT model on SQuAD 2.0 dataset, but you can train such model by yourself: all you need is to code dataset_reader for SQuAD 2.0 dataset or convert SQuAD 2.0 dataset to the same format as SQuAD 1.1 and use squad_bert.json config for training.