Never ending training about abstractive-summarization-with-transfer-learning

[Vibha111094]

Set your wamup steps to 10 percent of the total number of iterations required . In my case 15,000 helped. But please check .
Also please make sure you are sending the delimiter ie [SEP] as an indicator to stop decoding ie
labels_tgt = input_ids_tgt[1:]
input_ids_tgt = input_ids_tgt[:-1]
input_mask_src = [1] * len(input_ids_src)
input_mask_tgt = [1] * len(input_ids_tgt) while creating tf record .

[ishurironaldinho]

I'm running your code on the CNN/Dailymail dataset.

However, training never end, displaying :

Batch #X

with X growing more and more. I waited a long time, then kill the process.

But now, when I run the inference code, produced summary is very bad. Example :

the two - year - year - year - old cate - old cat was found in the animal .

What did I do wrong ? Anyone in the same situation who succeed to fix the code ? (@Vibha111094)

I run the inference code ,but i don't know how to produce the summary.

should i post the original story through the postman,so it will give back a summary???

[thatianafernandes]

Set your wamup steps to 10 percent of the total number of iterations required . In my case 15,000 helped. But please check .

Where exactly can I set that?

[Vibha111094]

In config.py you would have
lr = {
'learning_rate_schedule': 'constant.linear_warmup.rsqrt_decay.rsqrt_depth',
'lr_constant': 2 * (hidden_dim ** -0.5),
'static_lr': 1e-3,
'warmup_steps': 10000,
} .
You could increase to around 15000-20000.

[mishrachinmaya689]

When I put low numbers for steps =10 , warm up steps = 10 , max eval=10 iteration is still going 150+ for epoch 0. Could you help clarifying how those numbers are interlinked.

[xieyxclack]

Set your wamup steps to 10 percent of the total number of iterations required . In my case 15,000 helped. But please check .
Also please make sure you are sending the delimiter ie [SEP] as an indicator to stop decoding ie
labels_tgt = input_ids_tgt[1:]
input_ids_tgt = input_ids_tgt[:-1]
input_mask_src = [1] * len(input_ids_src)
input_mask_tgt = [1] * len(input_ids_tgt) while creating tf record .

hello, I adopt the default setting and obtain ROUGE-1/2/L: 39.29/17.30/27.10. In fact the ROUGE-L result is terrible. I trained on 1 GPU for 3 days, total 17w steps with batch size = 32.
Could you provide your results on CNN/Dailymail dataset, or do you know what is wrong?
Many thanks!@Vibha111094

[Shanzaay]

I am following the default settings. But after the second epoch, it's taking too long. Does anyone else happen to face the same problem?