Open atulkum opened 5 years ago
i very want to help you, but i only have a 1080ti 12g and don't know how change code to get BLEU score. sorry.
You can compare the rouge score too. I used 1070 with 8 gb and it took 3 days to train for 500k iteration. On 1080 ti it must be faster.
You can compare the rouge score too. I used 1070 with 8 gb and it took 3 days to train for 500k iteration. On 1080 ti it must be faster.
I have finished the test of 100K and am now doing another test of 500K.
Thats great. One more option would be to train for 700k make checkpoint every 50k and verify which checkpoint give best result.
Thats great. One more option would be to train for 700k make checkpoint every 50k and verify which checkpoint give best result.
I've done 288K/500K, and I will start the 700k test in 2 days. So I'm going to upload it to DropBox, or you can select one to me.
You don't need to upload the model. You can just report the rouge score.
You don't need to upload the model. You can just report the rouge score.
ok.
@atulkum did you try this model on some external data? like how do you convert just a csv file of text data to bin format. And could you upload pretrianed weight as well?? @pengzhi123
I'm sorry for uploading data now. Our machine is broken, and I only trained to 660K. The following is the experimental result: 100k (batch size 8): ROUGE-1: rouge_1_f_score: 0.3420 with confidence interval (0.3397, 0.3443) rouge_1_recall: 0.3830 with confidence interval (0.3803, 0.3856) rouge_1_precision: 0.3288 with confidence interval (0.3263, 0.3312)
ROUGE-2: rouge_2_f_score: 0.1401 with confidence interval (0.1382, 0.1420) rouge_2_recall: 0.1568 with confidence interval (0.1545, 0.1590) rouge_2_precision: 0.1350 with confidence interval (0.1331, 0.1369)
ROUGE-l: rouge_l_f_score: 0.3105 with confidence interval (0.3083, 0.3126) rouge_l_recall: 0.3475 with confidence interval (0.3448, 0.3500) rouge_l_precision: 0.2987 with confidence interval (0.2964, 0.3010)
500k (batch size 8): rouge_1_f_score: 0.3603 with confidence interval (0.3580, 0.3624) rouge_1_recall: 0.4006 with confidence interval (0.3980, 0.4032) rouge_1_precision: 0.3475 with confidence interval (0.3449, 0.3500)
ROUGE-2: rouge_2_f_score: 0.1538 with confidence interval (0.1515, 0.1560) rouge_2_recall: 0.1703 with confidence interval (0.1679, 0.1727) rouge_2_precision: 0.1492 with confidence interval (0.1469, 0.1514)
ROUGE-l: rouge_l_f_score: 0.3292 with confidence interval (0.3270, 0.3313) rouge_l_recall: 0.3659 with confidence interval (0.3633, 0.3684) rouge_l_precision: 0.3177 with confidence interval (0.3153, 0.3202)
Thanks for doing this. Did you enabled coverage loss for this result?
@pengzhi123 Hi there, Can I ask what machine u r running it on? Seems really fast
Need help for retraining and cross validation and see if the ROUGE score matches exactly (or better) with the numbers reported in the paper.
I just train for 500k iteration (with batch size 8) with pointer generation enabled + coverage loss disabled and next 100k iteration (with batch size 8) with pointer generation enabled + coverage loss enabled.
It would be great if someone can help re-running these experiments and try to see if we can improve the result and match it with the paper.
You might need a better GPU though. (my current one is gtx 1070 8 gb)