Yawei Sun

Hi, thank you for your reply. On this data, I have two confusions, which need your help: Firstly, I think machine maybe hardly understands which clue is useful, unless try...

Sorry, trouble you, again. After I set pretrain embedding requires_grad = False, it is ok. In detail, below: if vectors is not None: self.embedding_layer = nn.Embedding.from_pretrained(torch.FloatTensor(vectors)) self.embedding_layer.weight.requires_grad = **False** #True...

Hi, batch size = 4000, epoches = 100, other related hyperparameters are the same as the source. In specific: [lcquad] _neg_paths_per_epoch_train = 100 _neg_paths_per_epoch_validation = 1000 total_negative_samples = 1000 batch_size...

Sorry, trouble you. The result is below: BestValiAcc: 0.654. BestTestAcc: 0.664 In addition, when evaluate, RuntimeError: CUDA error: out of memory.  Would you help me to solve it ?...

ok, I have a try. But I have a question: how much epoch should be set ? thanks

I find 300 epochs in the paper. I have a try it.

Thanks for your suggestion. I can share these in Google Drive / Dropbox. Previously, I upload Google Drive, the speed was very slow. I will have a try Google Drive...

Hi, SPARQA update vision is here: https://github.com/nju-websoft/SkeletonKBQA. In SkeletonKBQA, we share resources in the Google Drive. Thanks for your advice. best,

您好，感谢您的关注 561.5324/2608 = 21.53 //2608是所有测试集的问句数量

您好，感谢您的关注 (1) 测试集规模是3531个问句 (2) 您计算 930.95176，猜测是消融实验结果SPARQA w/o sentence-level scorer 930.95176/3531=26.36 （但是不能确定） (3) 真实all_f1_score应该在1111多一点 1111/3531=31.46 您试试evaluation/kbcqa_evaluation.py中的两行切换一下，score 或 total_score试试看看 # score_to_queryid_sparql[grounded_graph.score].append(grounded_graph.grounded_query_id) #word level matcher score_to_queryid_sparql[grounded_graph.total_score].append(grounded_graph.grounded_query_id) thanks