Need help for retraining and cross validation about pointer_summarizer HOT 12 OPEN

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.

from pointer_summarizer.

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.

from pointer_summarizer.

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.

from pointer_summarizer.

Thats great. One more option would be to train for 700k make checkpoint every 50k and verify which checkpoint give best result.

from pointer_summarizer.

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.

from pointer_summarizer.

You don't need to upload the model. You can just report the rouge score.

from pointer_summarizer.

You don't need to upload the model. You can just report the rouge score.

ok.

from pointer_summarizer.

@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

from pointer_summarizer.

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)

from pointer_summarizer.

Thanks for doing this. Did you enabled coverage loss for this result?

from pointer_summarizer.

https://www.dropbox.com/s/czed99yiqjo34f3/training_log?dl=0

from pointer_summarizer.

@pengzhi123 Hi there, Can I ask what machine u r running it on? Seems really fast

from pointer_summarizer.