hello,author ,i am try to train a model with your code,the data used conll2012,i found it precise loss decrease, precise approximately equal to 0.

[mujizi]

Epoch: 150 | Loss: 2649.815816 | Mention recall: 0.054297 | Coref recall: 0.003106 | Coref precision: 0.000000

[VishwaasHegde]

I have the same issue, has anyone found a solution for that?

[sushantakpani]

My current : Epoch: 150 | Loss: 2366.799846 | Mention recall: 0.729597 | Coref recall: 0.673229 | Coref precision: 0.403507

[stavshem]

My current : Epoch: 150 | Loss: 2366.799846 | Mention recall: 0.729597 | Coref recall: 0.673229 | Coref precision: 0.403507

Hi, did you have to do any modifications to the training code to get these results?

[sushantakpani]

My current : Epoch: 150 | Loss: 2366.799846 | Mention recall: 0.729597 | Coref recall: 0.673229 | Coref precision: 0.403507

Hi, did you have to do any modifications to the training code to get these results?

Yes. First change: Please check my last reply on Jun 22 in #18

[stavshem]

My current : Epoch: 150 | Loss: 2366.799846 | Mention recall: 0.729597 | Coref recall: 0.673229 | Coref precision: 0.403507

Hi, did you have to do any modifications to the training code to get these results?

Yes. First change: Please check my last reply on Jun 22 in #18

Thank you! So I changed the loss according to your suggestion, using: loss = torch.sum(torch.log(torch.sum(torch.mul(probs, gold_indexes), dim=1).clamp_(eps, 1-eps)), dim=0) * -1 But model still does not converge, did you do any other changes besides that?

[sushantakpani]

My current : Epoch: 150 | Loss: 2366.799846 | Mention recall: 0.729597 | Coref recall: 0.673229 | Coref precision: 0.403507

Hi, did you have to do any modifications to the training code to get these results?

Yes. First change: Please check my last reply on Jun 22 in #18

Thank you! So I changed the loss according to your suggestion, using: loss = torch.sum(torch.log(torch.sum(torch.mul(probs, gold_indexes), dim=1).clamp_(eps, 1-eps)), dim=0) * -1 But model still does not converge, did you do any other changes besides that?

I also changed one line in coref.py file as suggested in issue #10 to handle index out of issue : self.train_corpus = [doc for doc in self.train_corpus if doc.sents] in def train_epoch(self, epoch):

[lizhuoranget]

Well, I modified these and finished train and evaluation but my result is poor as follows: Epoch: 150 | Loss: 2832.548317 | Mention recall: 0.067340 | Coref recall: 0.024316 | Coref precision: 0.020000. So did you sovle it?

My current : Epoch: 150 | Loss: 2366.799846 | Mention recall: 0.729597 | Coref recall: 0.673229 | Coref precision: 0.403507

Hi, did you have to do any modifications to the training code to get these results?

Yes. First change: Please check my last reply on Jun 22 in #18

Thank you! So I changed the loss according to your suggestion, using: loss = torch.sum(torch.log(torch.sum(torch.mul(probs, gold_indexes), dim=1).clamp_(eps, 1-eps)), dim=0) * -1 But model still does not converge, did you do any other changes besides that?

I also changed one line in coref.py file as suggested in issue #10 to handle index out of issue : self.train_corpus = [doc for doc in self.train_corpus if doc.sents] in def train_epoch(self, epoch):

My current : Epoch: 150 | Loss: 2366.799846 | Mention recall: 0.729597 | Coref recall: 0.673229 | Coref precision: 0.403507

Hi, did you have to do any modifications to the training code to get these results?

Yes. First change: Please check my last reply on Jun 22 in #18

Thank you! So I changed the loss according to your suggestion, using: loss = torch.sum(torch.log(torch.sum(torch.mul(probs, gold_indexes), dim=1).clamp_(eps, 1-eps)), dim=0) * -1 But model still does not converge, did you do any other changes besides that?