Encoder-Decoder model after fine tuning on Turkish dataset, generation gives the same results regardless of the input
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 : 
![output_1](https://user-images.githubusercontent.com/52102674/167256354-2b5bf0aa-9e7a-4a21-9512-85b32c6a33a2.jpg)
![output_2](https://user-images.githubusercontent.com/52102674/167256355-e8a727cd-f7f6-4f09-a330-c6a7e2c13635.jpg)

Here is the training loss : 
![train](https://user-images.githubusercontent.com/52102674/167256383-8681a062-5273-48de-9016-3382b789ff6e.jpg)

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! 
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?<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.<|||||>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-elshafey
> 
> It 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 : 

- https://github.com/huggingface/blog/issues/292
- https://github.com/huggingface/transformers/issues/17122

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!