chongyangtao / ioi Goto Github PK

Hello, Thanks for sharing the code, I have a small question, is the word embedding pre-trained on all the training data or truncated data (limit context turns and utterance length)?

Thanks for the code guys. Could you also share the exact data that was used to obtain the results reported in the paper (i.e., train, dev, test sets and vocabulary)?

In the mean time, I experimented with the Ubuntu Corpus dataset (ver2) and got following not-so-good results for the development set. Probably did something wrong when preparing the data...

Dev results..........
('pred_scores: ', 500000)
Step: 66000 	| loss: 2.805 	| acc: 0.860 	|  2019-09-15 13:27:36
recall_2_1:  0.904
recall_at_1: 0.657
recall_at_2: 0.800
recall_at_5: 0.949

Also, I think there is a minor bug in the code "main.py" (line 220): Definitions of variables "minimal_val_step" and "minimal_val_loss" are missing.

Does it support multi-GPU training?

Hello, I am going to reproduce your paper of IOI, but I have a trouble in downloading the data. Could you please share the ubuntu_data and its word2vec model in other ways that are available for those who in China?