Plan to publish the paper?

[monkdou0]

This is the great work for appling pre-train model to the task of paraphrase! Do you have the plan to publish a paper?

[Vamsi995]

Umm, I don't mind a paper but I'm thinking is this paper worthy?? Also if we can make a paper, how to go about it?

[monkdou0]

Maybe this is Lack of creativity a little. By the way, I want to ask the parameter in the training process: train_batch_size=6, eval_batch_size=6, num_train_epochs':1, is this the real used parameter?

[Vamsi995]

Yes, and the num_train_epochs is 2.

[Vamsi995]

I really thought of coming back to this and improving this further, but became a bit lazy.

[monkdou0]

tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws")

i don't see the code related to save the tokenizer? Is It same as t5-base tokenizer?

[Vamsi995]

Yeah its the same as the t5-base tokenizer.

[monkdou0]

Thank you very much! and when i run this code, I get this bug. the trainloss is nan and report this fault. what's wrong?

[Vamsi995]

If you are using a different dataset, you have to change the path to it in the T5FineTuner class, in the methods train_dataloader and val_dataloader

[monkdou0]

I already changed it. maybe it is not this problem. i see your code: in the 63line, is this right? it is self.forward()?

[Vamsi995]

No i dont think thats an issue. It should be self only.

[monkdou0]

Yeah! you are right, i know. but i run your dataset, your code, noting changed I print the outputs in this to debug and find it is empty list why?

[Vamsi995]

Let me figure this out. Right now I'm working on another project, it'll kind of take some time for me to get into this.

[chris1899]

Yeah its the same as the t5-base tokenizer.

Are you sure it is base? When I use the t5-base tokenizer I get error:

Exception: expected value at line 1 column 1

When I use t5-small tokenizer it works fine.

[Vamsi995]

pip install sentencepiece

from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("t5-base") # t5-base works
model = T5ForConditionalGeneration.from_pretrained("Vamsi/T5_Paraphrase_Paws")

sentence = "This is something which i cannot understand at all"
text =  "paraphrase: " + sentence
encoding = tokenizer(text,padding=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"]

outputs = model.generate(
    input_ids=input_ids, attention_mask=attention_masks,
    max_length=256,
    do_sample=True,
    top_k=200,
    top_p=0.95,
    early_stopping=True,
    num_return_sequences=5
)

for output in outputs:
    line = tokenizer.decode(output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
    print(line)

[monkdou0]

I use your code to train, except for ignore the training loss Is nan fault, same parameter,same dataset,same code but i get poor result compared with your model this is my result this is your model

I think this repo is useful for others, Please when you have space time, check your code thank you very much! good luck!