Closed AniketRajpoot closed 2 years ago
I highly recommend you get a workaround that works on the latest dataset and transformers library! I suffered a lot only for this reason.
I highly recommend you get a workaround that works on the latest dataset and transformers library! I suffered a lot only for this reason.
What do you mean by workaround? I am not sure what are you referring to?
you can look for the necessary changes that might be needed for the latest version and let me know if you get any?
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Hi bro, I met similar problems like you. Have you found any solution? Thanks very much.
I’m having the same issue. Did someone figure it out? Thanks in advance.
Edit: Solved by using the following library versions:
transformers==4.2.1
datasets==1.0.2
torch==1.6.0
credits to @salma-elshafey
I’m having the same issue. Did someone figure it out? Thanks in advance.
Edit: Solved by using the following library versions:
transformers==4.2.1
datasets==1.0.2
torch==1.6.0
credits to @salma-elshafey
It does not work for me :(. Could you please provide related links in your solution? Thanks very much. @mareloraby
I’m having the same issue. Did someone figure it out? Thanks in advance. Edit: Solved by using the following library versions:
transformers==4.2.1
datasets==1.0.2
torch==1.6.0
credits to @salma-elshafeyIt does not work for me :(. Could you please provide related links in your solution? Thanks very much. @mareloraby
Hey @tqnwhz, sorry I don’t have a reference I can link. My colleague who worked with an Encoder-Decoder model before helped me with that
Hello @tqnwhz! I hope you are doing well.
Can you take a look at following issues :
If these doesn't solve check out this blog : https://huggingface.co/blog/warm-starting-encoder-decoder#data-preprocessing
@AniketRajpoot Do you have no more issue with new transformer's version(s)?
Actually I did not train the same model but rather a different generation model based on codeBERT. But it was having some different issues. But it did solve the problem for random output and loss going instantly zero.
Thanks very much! @AniketRajpoot I'll conduct a few experiments to verify them.
@tqnwhz you could try this setting:
transformers==4.18.0
datasets==2.1.0
@AbuUbaida Thanks for your advice. I've tried this setting and it does not work :(. Given the fact that I've spent about two weeks trying to solve this but in vain. I plan to turn to other approaches rather than seq2seq.
Thanks again for your advices sincerely. Hope you everything goes well.
Hi @tqnwhz , could you provide the script and the datasets that could reproduce this issue. As this issue seems to happen a few times, I think it would be great if we can find the cause and fix it. But I need something that could reproduce it 🙏 Thank you.
Hi @ydshieh , I'm afraid that my code and dataset are not typical for this problem, since I try to use seq2seq to model multi-label text classification, rather than normal text generation task.
OK, no problem!
Hello everyone, I need help with the training of the encoder-decoder model. I need to fine-tune a bert2bert for Turkish content summarization. I am using this sample notebook reference: https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb
After training on my custom dataset, when I generate using a test dataset, I get gibberish results regardless of the amount of training. I have attached the results below and one more observation I made is that the training loss instantly goes to near 0 values after a few steps of training I am not sure what I am doing wrong.
Here are the screenshots of output :
Here is the training loss :
Here is the full notebook that I used for finetuning : https://colab.research.google.com/drive/188Lil4Uc3wY7nd1PEfCjMwSfPO-NXI94?usp=sharing
I am not sure what I am doing wrong? I would be grateful for any advice. Thank you!